diff --git a/docs/proposals/algorithms/[OSPP]GAN and Self-taught Learning/[OSPP]A Proposal of Integrating GAN and Self-taught Learning to ianvs.md b/docs/proposals/algorithms/[OSPP]GAN and Self-taught Learning/[OSPP]A Proposal of Integrating GAN and Self-taught Learning to ianvs.md
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+# Integrate GAN and Self-taught Learning into ianvs Lifelong Learning to Handle Unknown Tasks
+
+## Motivation
+
+In the process of ianvs lifelong learning, there would be a chance to confront unknown tasks, whose data are always heterogeneous small sample. Generate Adversarial Networks(GAN) is the start-of-art generative model and GAN can generate fake data according to the distribution of the real data. Naturally, we try to utilize GAN to handle small sample problem. Self-taught learning is an approach to improve classfication performance using sparse coding to construct higher-level features with the unlabeled data. Hence, we combine GAN and self-taught learning to help ianvs lifelong learning handle unknown tasks.
+
+### Goals
+
+* Handle unknown tasks
+* Implement of a lightweight GAN to solve small sample problem
+* Utilize self-taught learning to solve heterogeneous problem
+
+## Proposal
+We focus on the process of handling unknown tasks.     
+
+The overview is as follows:
+
+![](images/overview.png)
+
+The process is illustrated as below:    
+1. GAN exploits the unknown task sample to generate more fake sample. 
+2. Self-taught learning unit utilize the fake sample and orginal unknown task sample and its label to train a classifier.
+3. A well trained classifier is output.
+
+### GAN Design
+We use the networks design by [TOWARDS FASTER AND STABILIZED GAN TRAINING FOR HIGH-FIDELITY FEW-SHOT IMAGE SYNTHESIS](https://openreview.net/forum?id=1Fqg133qRaI). The design is aimed for small training data and pour computing devices. Therefore, it is perfectly suitable for handling unkwnon tasks of ianvs lifelong learning. The networks is shown below.    
+
+![](images/GAN.png)
+
+### Self-taught Learing Design
+Self-taught learning uses unlabeled data to find the latent feature of data and then makes every labeled data a represention using the latent feature and uses the represention and label corresponding to train classifier.    
+
+![](images/self-taught%20learning.png)
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diff --git a/docs/proposals/algorithms/[OSPP]GAN and Self-taught Learning/images/self-taught learning.png b/docs/proposals/algorithms/[OSPP]GAN and Self-taught Learning/images/self-taught learning.png
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diff --git a/examples/GANwithSelf-taughtLearning/GAN/__init__.py b/examples/GANwithSelf-taughtLearning/GAN/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/GANwithSelf-taughtLearning/GAN/diffaug.py b/examples/GANwithSelf-taughtLearning/GAN/diffaug.py
new file mode 100644
index 00000000..54c0894f
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/diffaug.py
@@ -0,0 +1,76 @@
+# Differentiable Augmentation for Data-Efficient GAN Training
+# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han
+# https://arxiv.org/pdf/2006.10738
+
+import torch
+import torch.nn.functional as F
+
+
+def DiffAugment(x, policy='', channels_first=True):
+    if policy:
+        if not channels_first:
+            x = x.permute(0, 3, 1, 2)
+        for p in policy.split(','):
+            for f in AUGMENT_FNS[p]:
+                x = f(x)
+        if not channels_first:
+            x = x.permute(0, 2, 3, 1)
+        x = x.contiguous()
+    return x
+
+
+def rand_brightness(x):
+    x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
+    return x
+
+
+def rand_saturation(x):
+    x_mean = x.mean(dim=1, keepdim=True)
+    x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean
+    return x
+
+
+def rand_contrast(x):
+    x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
+    x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean
+    return x
+
+
+def rand_translation(x, ratio=0.125):
+    shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
+    translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
+    translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
+    grid_batch, grid_x, grid_y = torch.meshgrid(
+        torch.arange(x.size(0), dtype=torch.long, device=x.device),
+        torch.arange(x.size(2), dtype=torch.long, device=x.device),
+        torch.arange(x.size(3), dtype=torch.long, device=x.device),
+    )
+    grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
+    grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
+    x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
+    x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
+    return x
+
+
+def rand_cutout(x, ratio=0.5):
+    cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
+    offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device)
+    offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device)
+    grid_batch, grid_x, grid_y = torch.meshgrid(
+        torch.arange(x.size(0), dtype=torch.long, device=x.device),
+        torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
+        torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
+    )
+    grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
+    grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
+    mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
+    mask[grid_batch, grid_x, grid_y] = 0
+    x = x * mask.unsqueeze(1)
+    return x
+
+
+AUGMENT_FNS = {
+    'color': [rand_brightness, rand_saturation, rand_contrast],
+    'translation': [rand_translation],
+    'cutout': [rand_cutout],
+}
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diff --git a/examples/GANwithSelf-taughtLearning/GAN/generate_fake_imgs.py b/examples/GANwithSelf-taughtLearning/GAN/generate_fake_imgs.py
new file mode 100644
index 00000000..4f0bb609
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/generate_fake_imgs.py
@@ -0,0 +1,46 @@
+import torch
+
+from models import Generator, weights_init
+
+import matplotlib.pyplot as plt
+
+import os
+
+from collections import OrderedDict
+
+import numpy as np
+
+from skimage import io
+
+# print(os.getcwd())
+
+device = 'cuda'
+
+ngf = 64
+nz = 256
+im_size = 1024
+netG = Generator(ngf=ngf, nz=nz, im_size=im_size).to(device)
+weights_init(netG)
+weights = torch.load(os.getcwd() + '/train_results/test1/models/50000.pth')
+# print(weights['g'])
+netG_weights = OrderedDict()
+for name, weight in weights['g'].items():
+    name = name.split('.')[1:]
+    name = '.'.join(name)
+    netG_weights[name] = weight
+netG.load_state_dict(netG_weights)
+current_batch_size = 1
+
+
+index = 1
+while index <= 3000:
+    noise = torch.Tensor(current_batch_size, nz).normal_(0, 1).to(device)
+    fake_images = netG(noise)[0]
+    for fake_image in fake_images:
+        fake_image = fake_image.detach().cpu().numpy().transpose(1, 2, 0)
+        fake_image = fake_image * np.array([0.5, 0.5, 0.5])
+        fake_image = fake_image + np.array([0.5, 0.5, 0.5])
+        fake_image = (fake_image * 255).astype(np.uint8)
+        io.imsave('../data/fake_imgs1/' + str(index) + '.png', fake_image)
+        print('figure {} done'.format(index))
+        index += 1
\ No newline at end of file
diff --git a/examples/GANwithSelf-taughtLearning/GAN/lpips/__init__.py b/examples/GANwithSelf-taughtLearning/GAN/lpips/__init__.py
new file mode 100644
index 00000000..1878b3ff
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/lpips/__init__.py
@@ -0,0 +1,168 @@
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import skimage
+import torch
+from torch.autograd import Variable
+
+from lpips import dist_model
+
+
+if skimage.__version__ == '0.14.3':
+    from skimage.measure import compare_ssim
+else:
+    from skimage.metrics import structural_similarity as compare_ssim
+
+
+
+class PerceptualLoss(torch.nn.Module):
+    def __init__(self, model='net-lin', net='alex', colorspace='rgb', spatial=False, use_gpu=True, gpu_ids=[0]): # VGG using our perceptually-learned weights (LPIPS metric)
+    # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss
+        super(PerceptualLoss, self).__init__()
+        print('Setting up Perceptual loss...')
+        self.use_gpu = use_gpu
+        self.spatial = spatial
+        self.gpu_ids = gpu_ids
+        self.model = dist_model.DistModel()
+        self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace, spatial=self.spatial, gpu_ids=gpu_ids)
+        print('...[%s] initialized'%self.model.name())
+        print('...Done')
+
+    def forward(self, pred, target, normalize=False):
+        """
+        Pred and target are Variables.
+        If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1]
+        If normalize is False, assumes the images are already between [-1,+1]
+
+        Inputs pred and target are Nx3xHxW
+        Output pytorch Variable N long
+        """
+
+        if normalize:
+            target = 2 * target  - 1
+            pred = 2 * pred  - 1
+
+        return self.model.forward(target, pred)
+
+def normalize_tensor(in_feat,eps=1e-10):
+    norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True))
+    return in_feat/(norm_factor+eps)
+
+def l2(p0, p1, range=255.):
+    return .5*np.mean((p0 / range - p1 / range)**2)
+
+def psnr(p0, p1, peak=255.):
+    return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2))
+
+def dssim(p0, p1, range=255.):
+    return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
+
+def rgb2lab(in_img,mean_cent=False):
+    from skimage import color
+    img_lab = color.rgb2lab(in_img)
+    if(mean_cent):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+    return img_lab
+
+def tensor2np(tensor_obj):
+    # change dimension of a tensor object into a numpy array
+    return tensor_obj[0].cpu().float().numpy().transpose((1,2,0))
+
+def np2tensor(np_obj):
+     # change dimenion of np array into tensor array
+    return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
+
+def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False):
+    # image tensor to lab tensor
+    from skimage import color
+
+    img = tensor2im(image_tensor)
+    img_lab = color.rgb2lab(img)
+    if(mc_only):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+    if(to_norm and not mc_only):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+        img_lab = img_lab/100.
+
+    return np2tensor(img_lab)
+
+def tensorlab2tensor(lab_tensor,return_inbnd=False):
+    from skimage import color
+    import warnings
+    warnings.filterwarnings("ignore")
+
+    lab = tensor2np(lab_tensor)*100.
+    lab[:,:,0] = lab[:,:,0]+50
+
+    rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1)
+    if(return_inbnd):
+        # convert back to lab, see if we match
+        lab_back = color.rgb2lab(rgb_back.astype('uint8'))
+        mask = 1.*np.isclose(lab_back,lab,atol=2.)
+        mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis])
+        return (im2tensor(rgb_back),mask)
+    else:
+        return im2tensor(rgb_back)
+
+def rgb2lab(input):
+    from skimage import color
+    return color.rgb2lab(input / 255.)
+
+def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
+    image_numpy = image_tensor[0].cpu().float().numpy()
+    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
+    return image_numpy.astype(imtype)
+
+def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
+    return torch.Tensor((image / factor - cent)
+                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
+
+def tensor2vec(vector_tensor):
+    return vector_tensor.data.cpu().numpy()[:, :, 0, 0]
+
+def voc_ap(rec, prec, use_07_metric=False):
+    """ ap = voc_ap(rec, prec, [use_07_metric])
+    Compute VOC AP given precision and recall.
+    If use_07_metric is true, uses the
+    VOC 07 11 point method (default:False).
+    """
+    if use_07_metric:
+        # 11 point metric
+        ap = 0.
+        for t in np.arange(0., 1.1, 0.1):
+            if np.sum(rec >= t) == 0:
+                p = 0
+            else:
+                p = np.max(prec[rec >= t])
+            ap = ap + p / 11.
+    else:
+        # correct AP calculation
+        # first append sentinel values at the end
+        mrec = np.concatenate(([0.], rec, [1.]))
+        mpre = np.concatenate(([0.], prec, [0.]))
+
+        # compute the precision envelope
+        for i in range(mpre.size - 1, 0, -1):
+            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
+
+        # to calculate area under PR curve, look for points
+        # where X axis (recall) changes value
+        i = np.where(mrec[1:] != mrec[:-1])[0]
+
+        # and sum (\Delta recall) * prec
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
+    return ap
+
+def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
+# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):
+    image_numpy = image_tensor[0].cpu().float().numpy()
+    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
+    return image_numpy.astype(imtype)
+
+def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
+# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.):
+    return torch.Tensor((image / factor - cent)
+                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
diff --git a/examples/GANwithSelf-taughtLearning/GAN/lpips/base_model.py b/examples/GANwithSelf-taughtLearning/GAN/lpips/base_model.py
new file mode 100644
index 00000000..9fdb9306
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/lpips/base_model.py
@@ -0,0 +1,58 @@
+import os
+import torch
+from torch.autograd import Variable
+from pdb import set_trace as st
+from IPython import embed
+
+class BaseModel():
+    def __init__(self):
+        pass;
+        
+    def name(self):
+        return 'BaseModel'
+
+    def initialize(self, use_gpu=True, gpu_ids=[0]):
+        self.use_gpu = use_gpu
+        self.gpu_ids = gpu_ids
+
+    def forward(self):
+        pass
+
+    def get_image_paths(self):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        return self.input
+
+    def get_current_errors(self):
+        return {}
+
+    def save(self, label):
+        pass
+
+    # helper saving function that can be used by subclasses
+    def save_network(self, network, path, network_label, epoch_label):
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(path, save_filename)
+        torch.save(network.state_dict(), save_path)
+
+    # helper loading function that can be used by subclasses
+    def load_network(self, network, network_label, epoch_label):
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        print('Loading network from %s'%save_path)
+        network.load_state_dict(torch.load(save_path))
+
+    def update_learning_rate():
+        pass
+
+    def get_image_paths(self):
+        return self.image_paths
+
+    def save_done(self, flag=False):
+        np.save(os.path.join(self.save_dir, 'done_flag'),flag)
+        np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i')
+
diff --git a/examples/GANwithSelf-taughtLearning/GAN/lpips/dist_model.py b/examples/GANwithSelf-taughtLearning/GAN/lpips/dist_model.py
new file mode 100644
index 00000000..4ff0aa4c
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/lpips/dist_model.py
@@ -0,0 +1,284 @@
+
+from __future__ import absolute_import
+
+import sys
+import numpy as np
+import torch
+from torch import nn
+import os
+from collections import OrderedDict
+from torch.autograd import Variable
+import itertools
+from .base_model import BaseModel
+from scipy.ndimage import zoom
+import fractions
+import functools
+import skimage.transform
+from tqdm import tqdm
+
+from IPython import embed
+
+from . import networks_basic as networks
+import lpips as util
+
+class DistModel(BaseModel):
+    def name(self):
+        return self.model_name
+
+    def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False, model_path=None,
+            use_gpu=True, printNet=False, spatial=False, 
+            is_train=False, lr=.0001, beta1=0.5, version='0.1', gpu_ids=[0]):
+        '''
+        INPUTS
+            model - ['net-lin'] for linearly calibrated network
+                    ['net'] for off-the-shelf network
+                    ['L2'] for L2 distance in Lab colorspace
+                    ['SSIM'] for ssim in RGB colorspace
+            net - ['squeeze','alex','vgg']
+            model_path - if None, will look in weights/[NET_NAME].pth
+            colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM
+            use_gpu - bool - whether or not to use a GPU
+            printNet - bool - whether or not to print network architecture out
+            spatial - bool - whether to output an array containing varying distances across spatial dimensions
+            spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below).
+            spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images.
+            spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear).
+            is_train - bool - [True] for training mode
+            lr - float - initial learning rate
+            beta1 - float - initial momentum term for adam
+            version - 0.1 for latest, 0.0 was original (with a bug)
+            gpu_ids - int array - [0] by default, gpus to use
+        '''
+        BaseModel.initialize(self, use_gpu=use_gpu, gpu_ids=gpu_ids)
+
+        self.model = model
+        self.net = net
+        self.is_train = is_train
+        self.spatial = spatial
+        self.gpu_ids = gpu_ids
+        self.model_name = '%s [%s]'%(model,net)
+
+        if(self.model == 'net-lin'): # pretrained net + linear layer
+            self.net = networks.PNetLin(pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net,
+                use_dropout=True, spatial=spatial, version=version, lpips=True)
+            kw = {}
+            if not use_gpu:
+                kw['map_location'] = 'cpu'
+            if(model_path is None):
+                import inspect
+                model_path = os.path.abspath(os.path.join(inspect.getfile(self.initialize), '..', 'weights/v%s/%s.pth'%(version,net)))
+
+            if(not is_train):
+                print('Loading model from: %s'%model_path)
+                self.net.load_state_dict(torch.load(model_path, **kw), strict=False)
+
+        elif(self.model=='net'): # pretrained network
+            self.net = networks.PNetLin(pnet_rand=pnet_rand, pnet_type=net, lpips=False)
+        elif(self.model in ['L2','l2']):
+            self.net = networks.L2(use_gpu=use_gpu,colorspace=colorspace) # not really a network, only for testing
+            self.model_name = 'L2'
+        elif(self.model in ['DSSIM','dssim','SSIM','ssim']):
+            self.net = networks.DSSIM(use_gpu=use_gpu,colorspace=colorspace)
+            self.model_name = 'SSIM'
+        else:
+            raise ValueError("Model [%s] not recognized." % self.model)
+
+        self.parameters = list(self.net.parameters())
+
+        if self.is_train: # training mode
+            # extra network on top to go from distances (d0,d1) => predicted human judgment (h*)
+            self.rankLoss = networks.BCERankingLoss()
+            self.parameters += list(self.rankLoss.net.parameters())
+            self.lr = lr
+            self.old_lr = lr
+            self.optimizer_net = torch.optim.Adam(self.parameters, lr=lr, betas=(beta1, 0.999))
+        else: # test mode
+            self.net.eval()
+
+        if(use_gpu):
+            self.net.to(gpu_ids[0])
+            self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids)
+            if(self.is_train):
+                self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0
+
+        if(printNet):
+            print('---------- Networks initialized -------------')
+            networks.print_network(self.net)
+            print('-----------------------------------------------')
+
+    def forward(self, in0, in1, retPerLayer=False):
+        ''' Function computes the distance between image patches in0 and in1
+        INPUTS
+            in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1]
+        OUTPUT
+            computed distances between in0 and in1
+        '''
+
+        return self.net.forward(in0, in1, retPerLayer=retPerLayer)
+
+    # ***** TRAINING FUNCTIONS *****
+    def optimize_parameters(self):
+        self.forward_train()
+        self.optimizer_net.zero_grad()
+        self.backward_train()
+        self.optimizer_net.step()
+        self.clamp_weights()
+
+    def clamp_weights(self):
+        for module in self.net.modules():
+            if(hasattr(module, 'weight') and module.kernel_size==(1,1)):
+                module.weight.data = torch.clamp(module.weight.data,min=0)
+
+    def set_input(self, data):
+        self.input_ref = data['ref']
+        self.input_p0 = data['p0']
+        self.input_p1 = data['p1']
+        self.input_judge = data['judge']
+
+        if(self.use_gpu):
+            self.input_ref = self.input_ref.to(device=self.gpu_ids[0])
+            self.input_p0 = self.input_p0.to(device=self.gpu_ids[0])
+            self.input_p1 = self.input_p1.to(device=self.gpu_ids[0])
+            self.input_judge = self.input_judge.to(device=self.gpu_ids[0])
+
+        self.var_ref = Variable(self.input_ref,requires_grad=True)
+        self.var_p0 = Variable(self.input_p0,requires_grad=True)
+        self.var_p1 = Variable(self.input_p1,requires_grad=True)
+
+    def forward_train(self): # run forward pass
+        # print(self.net.module.scaling_layer.shift)
+        # print(torch.norm(self.net.module.net.slice1[0].weight).item(), torch.norm(self.net.module.lin0.model[1].weight).item())
+
+        self.d0 = self.forward(self.var_ref, self.var_p0)
+        self.d1 = self.forward(self.var_ref, self.var_p1)
+        self.acc_r = self.compute_accuracy(self.d0,self.d1,self.input_judge)
+
+        self.var_judge = Variable(1.*self.input_judge).view(self.d0.size())
+
+        self.loss_total = self.rankLoss.forward(self.d0, self.d1, self.var_judge*2.-1.)
+
+        return self.loss_total
+
+    def backward_train(self):
+        torch.mean(self.loss_total).backward()
+
+    def compute_accuracy(self,d0,d1,judge):
+        ''' d0, d1 are Variables, judge is a Tensor '''
+        d1_lt_d0 = (d1<d0).cpu().data.numpy().flatten()
+        judge_per = judge.cpu().numpy().flatten()
+        return d1_lt_d0*judge_per + (1-d1_lt_d0)*(1-judge_per)
+
+    def get_current_errors(self):
+        retDict = OrderedDict([('loss_total', self.loss_total.data.cpu().numpy()),
+                            ('acc_r', self.acc_r)])
+
+        for key in retDict.keys():
+            retDict[key] = np.mean(retDict[key])
+
+        return retDict
+
+    def get_current_visuals(self):
+        zoom_factor = 256/self.var_ref.data.size()[2]
+
+        ref_img = util.tensor2im(self.var_ref.data)
+        p0_img = util.tensor2im(self.var_p0.data)
+        p1_img = util.tensor2im(self.var_p1.data)
+
+        ref_img_vis = zoom(ref_img,[zoom_factor, zoom_factor, 1],order=0)
+        p0_img_vis = zoom(p0_img,[zoom_factor, zoom_factor, 1],order=0)
+        p1_img_vis = zoom(p1_img,[zoom_factor, zoom_factor, 1],order=0)
+
+        return OrderedDict([('ref', ref_img_vis),
+                            ('p0', p0_img_vis),
+                            ('p1', p1_img_vis)])
+
+    def save(self, path, label):
+        if(self.use_gpu):
+            self.save_network(self.net.module, path, '', label)
+        else:
+            self.save_network(self.net, path, '', label)
+        self.save_network(self.rankLoss.net, path, 'rank', label)
+
+    def update_learning_rate(self,nepoch_decay):
+        lrd = self.lr / nepoch_decay
+        lr = self.old_lr - lrd
+
+        for param_group in self.optimizer_net.param_groups:
+            param_group['lr'] = lr
+
+        print('update lr [%s] decay: %f -> %f' % (type,self.old_lr, lr))
+        self.old_lr = lr
+
+def score_2afc_dataset(data_loader, func, name=''):
+    ''' Function computes Two Alternative Forced Choice (2AFC) score using
+        distance function 'func' in dataset 'data_loader'
+    INPUTS
+        data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside
+        func - callable distance function - calling d=func(in0,in1) should take 2
+            pytorch tensors with shape Nx3xXxY, and return numpy array of length N
+    OUTPUTS
+        [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators
+        [1] - dictionary with following elements
+            d0s,d1s - N arrays containing distances between reference patch to perturbed patches 
+            gts - N array in [0,1], preferred patch selected by human evaluators
+                (closer to "0" for left patch p0, "1" for right patch p1,
+                "0.6" means 60pct people preferred right patch, 40pct preferred left)
+            scores - N array in [0,1], corresponding to what percentage function agreed with humans
+    CONSTS
+        N - number of test triplets in data_loader
+    '''
+
+    d0s = []
+    d1s = []
+    gts = []
+
+    for data in tqdm(data_loader.load_data(), desc=name):
+        d0s+=func(data['ref'],data['p0']).data.cpu().numpy().flatten().tolist()
+        d1s+=func(data['ref'],data['p1']).data.cpu().numpy().flatten().tolist()
+        gts+=data['judge'].cpu().numpy().flatten().tolist()
+
+    d0s = np.array(d0s)
+    d1s = np.array(d1s)
+    gts = np.array(gts)
+    scores = (d0s<d1s)*(1.-gts) + (d1s<d0s)*gts + (d1s==d0s)*.5
+
+    return(np.mean(scores), dict(d0s=d0s,d1s=d1s,gts=gts,scores=scores))
+
+def score_jnd_dataset(data_loader, func, name=''):
+    ''' Function computes JND score using distance function 'func' in dataset 'data_loader'
+    INPUTS
+        data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside
+        func - callable distance function - calling d=func(in0,in1) should take 2
+            pytorch tensors with shape Nx3xXxY, and return pytorch array of length N
+    OUTPUTS
+        [0] - JND score in [0,1], mAP score (area under precision-recall curve)
+        [1] - dictionary with following elements
+            ds - N array containing distances between two patches shown to human evaluator
+            sames - N array containing fraction of people who thought the two patches were identical
+    CONSTS
+        N - number of test triplets in data_loader
+    '''
+
+    ds = []
+    gts = []
+
+    for data in tqdm(data_loader.load_data(), desc=name):
+        ds+=func(data['p0'],data['p1']).data.cpu().numpy().tolist()
+        gts+=data['same'].cpu().numpy().flatten().tolist()
+
+    sames = np.array(gts)
+    ds = np.array(ds)
+
+    sorted_inds = np.argsort(ds)
+    ds_sorted = ds[sorted_inds]
+    sames_sorted = sames[sorted_inds]
+
+    TPs = np.cumsum(sames_sorted)
+    FPs = np.cumsum(1-sames_sorted)
+    FNs = np.sum(sames_sorted)-TPs
+
+    precs = TPs/(TPs+FPs)
+    recs = TPs/(TPs+FNs)
+    score = util.voc_ap(recs,precs)
+
+    return(score, dict(ds=ds,sames=sames))
diff --git a/examples/GANwithSelf-taughtLearning/GAN/lpips/networks_basic.py b/examples/GANwithSelf-taughtLearning/GAN/lpips/networks_basic.py
new file mode 100644
index 00000000..1d23f059
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/lpips/networks_basic.py
@@ -0,0 +1,187 @@
+
+from __future__ import absolute_import
+
+import sys
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import numpy as np
+from pdb import set_trace as st
+from skimage import color
+from IPython import embed
+from . import pretrained_networks as pn
+
+import lpips as util
+
+def spatial_average(in_tens, keepdim=True):
+    return in_tens.mean([2,3],keepdim=keepdim)
+
+def upsample(in_tens, out_H=64): # assumes scale factor is same for H and W
+    in_H = in_tens.shape[2]
+    scale_factor = 1.*out_H/in_H
+
+    return nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False)(in_tens)
+
+# Learned perceptual metric
+class PNetLin(nn.Module):
+    def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False, version='0.1', lpips=True):
+        super(PNetLin, self).__init__()
+
+        self.pnet_type = pnet_type
+        self.pnet_tune = pnet_tune
+        self.pnet_rand = pnet_rand
+        self.spatial = spatial
+        self.lpips = lpips
+        self.version = version
+        self.scaling_layer = ScalingLayer()
+
+        if(self.pnet_type in ['vgg','vgg16']):
+            net_type = pn.vgg16
+            self.chns = [64,128,256,512,512]
+        elif(self.pnet_type=='alex'):
+            net_type = pn.alexnet
+            self.chns = [64,192,384,256,256]
+        elif(self.pnet_type=='squeeze'):
+            net_type = pn.squeezenet
+            self.chns = [64,128,256,384,384,512,512]
+        self.L = len(self.chns)
+
+        self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune)
+
+        if(lpips):
+            self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
+            self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
+            self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
+            self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
+            self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
+            self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
+            if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
+                self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)
+                self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)
+                self.lins+=[self.lin5,self.lin6]
+
+    def forward(self, in0, in1, retPerLayer=False):
+        # v0.0 - original release had a bug, where input was not scaled
+        in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version=='0.1' else (in0, in1)
+        outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input)
+        feats0, feats1, diffs = {}, {}, {}
+
+        for kk in range(self.L):
+            feats0[kk], feats1[kk] = util.normalize_tensor(outs0[kk]), util.normalize_tensor(outs1[kk])
+            diffs[kk] = (feats0[kk]-feats1[kk])**2
+
+        if(self.lpips):
+            if(self.spatial):
+                res = [upsample(self.lins[kk].model(diffs[kk]), out_H=in0.shape[2]) for kk in range(self.L)]
+            else:
+                res = [spatial_average(self.lins[kk].model(diffs[kk]), keepdim=True) for kk in range(self.L)]
+        else:
+            if(self.spatial):
+                res = [upsample(diffs[kk].sum(dim=1,keepdim=True), out_H=in0.shape[2]) for kk in range(self.L)]
+            else:
+                res = [spatial_average(diffs[kk].sum(dim=1,keepdim=True), keepdim=True) for kk in range(self.L)]
+
+        val = res[0]
+        for l in range(1,self.L):
+            val += res[l]
+        
+        if(retPerLayer):
+            return (val, res)
+        else:
+            return val
+
+class ScalingLayer(nn.Module):
+    def __init__(self):
+        super(ScalingLayer, self).__init__()
+        self.register_buffer('shift', torch.Tensor([-.030,-.088,-.188])[None,:,None,None])
+        self.register_buffer('scale', torch.Tensor([.458,.448,.450])[None,:,None,None])
+
+    def forward(self, inp):
+        return (inp - self.shift) / self.scale
+
+
+class NetLinLayer(nn.Module):
+    ''' A single linear layer which does a 1x1 conv '''
+    def __init__(self, chn_in, chn_out=1, use_dropout=False):
+        super(NetLinLayer, self).__init__()
+
+        layers = [nn.Dropout(),] if(use_dropout) else []
+        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),]
+        self.model = nn.Sequential(*layers)
+
+
+class Dist2LogitLayer(nn.Module):
+    ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) '''
+    def __init__(self, chn_mid=32, use_sigmoid=True):
+        super(Dist2LogitLayer, self).__init__()
+
+        layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True),]
+        layers += [nn.LeakyReLU(0.2,True),]
+        layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True),]
+        layers += [nn.LeakyReLU(0.2,True),]
+        layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True),]
+        if(use_sigmoid):
+            layers += [nn.Sigmoid(),]
+        self.model = nn.Sequential(*layers)
+
+    def forward(self,d0,d1,eps=0.1):
+        return self.model.forward(torch.cat((d0,d1,d0-d1,d0/(d1+eps),d1/(d0+eps)),dim=1))
+
+class BCERankingLoss(nn.Module):
+    def __init__(self, chn_mid=32):
+        super(BCERankingLoss, self).__init__()
+        self.net = Dist2LogitLayer(chn_mid=chn_mid)
+        # self.parameters = list(self.net.parameters())
+        self.loss = torch.nn.BCELoss()
+
+    def forward(self, d0, d1, judge):
+        per = (judge+1.)/2.
+        self.logit = self.net.forward(d0,d1)
+        return self.loss(self.logit, per)
+
+# L2, DSSIM metrics
+class FakeNet(nn.Module):
+    def __init__(self, use_gpu=True, colorspace='Lab'):
+        super(FakeNet, self).__init__()
+        self.use_gpu = use_gpu
+        self.colorspace=colorspace
+
+class L2(FakeNet):
+
+    def forward(self, in0, in1, retPerLayer=None):
+        assert(in0.size()[0]==1) # currently only supports batchSize 1
+
+        if(self.colorspace=='RGB'):
+            (N,C,X,Y) = in0.size()
+            value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N)
+            return value
+        elif(self.colorspace=='Lab'):
+            value = util.l2(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)), 
+                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
+            ret_var = Variable( torch.Tensor((value,) ) )
+            if(self.use_gpu):
+                ret_var = ret_var.cuda()
+            return ret_var
+
+class DSSIM(FakeNet):
+
+    def forward(self, in0, in1, retPerLayer=None):
+        assert(in0.size()[0]==1) # currently only supports batchSize 1
+
+        if(self.colorspace=='RGB'):
+            value = util.dssim(1.*util.tensor2im(in0.data), 1.*util.tensor2im(in1.data), range=255.).astype('float')
+        elif(self.colorspace=='Lab'):
+            value = util.dssim(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)), 
+                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
+        ret_var = Variable( torch.Tensor((value,) ) )
+        if(self.use_gpu):
+            ret_var = ret_var.cuda()
+        return ret_var
+
+def print_network(net):
+    num_params = 0
+    for param in net.parameters():
+        num_params += param.numel()
+    print('Network',net)
+    print('Total number of parameters: %d' % num_params)
diff --git a/examples/GANwithSelf-taughtLearning/GAN/lpips/pretrained_networks.py b/examples/GANwithSelf-taughtLearning/GAN/lpips/pretrained_networks.py
new file mode 100644
index 00000000..077a2441
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/lpips/pretrained_networks.py
@@ -0,0 +1,181 @@
+from collections import namedtuple
+import torch
+from torchvision import models as tv
+from IPython import embed
+
+class squeezenet(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(squeezenet, self).__init__()
+        pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.slice6 = torch.nn.Sequential()
+        self.slice7 = torch.nn.Sequential()
+        self.N_slices = 7
+        for x in range(2):
+            self.slice1.add_module(str(x), pretrained_features[x])
+        for x in range(2,5):
+            self.slice2.add_module(str(x), pretrained_features[x])
+        for x in range(5, 8):
+            self.slice3.add_module(str(x), pretrained_features[x])
+        for x in range(8, 10):
+            self.slice4.add_module(str(x), pretrained_features[x])
+        for x in range(10, 11):
+            self.slice5.add_module(str(x), pretrained_features[x])
+        for x in range(11, 12):
+            self.slice6.add_module(str(x), pretrained_features[x])
+        for x in range(12, 13):
+            self.slice7.add_module(str(x), pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1 = h
+        h = self.slice2(h)
+        h_relu2 = h
+        h = self.slice3(h)
+        h_relu3 = h
+        h = self.slice4(h)
+        h_relu4 = h
+        h = self.slice5(h)
+        h_relu5 = h
+        h = self.slice6(h)
+        h_relu6 = h
+        h = self.slice7(h)
+        h_relu7 = h
+        vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7'])
+        out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7)
+
+        return out
+
+
+class alexnet(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(alexnet, self).__init__()
+        alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(2):
+            self.slice1.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(2, 5):
+            self.slice2.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(5, 8):
+            self.slice3.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(8, 10):
+            self.slice4.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(10, 12):
+            self.slice5.add_module(str(x), alexnet_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1 = h
+        h = self.slice2(h)
+        h_relu2 = h
+        h = self.slice3(h)
+        h_relu3 = h
+        h = self.slice4(h)
+        h_relu4 = h
+        h = self.slice5(h)
+        h_relu5 = h
+        alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
+        out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
+
+        return out
+
+class vgg16(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(vgg16, self).__init__()
+        vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
+        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+
+        return out
+
+
+
+class resnet(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True, num=18):
+        super(resnet, self).__init__()
+        if(num==18):
+            self.net = tv.resnet18(pretrained=pretrained)
+        elif(num==34):
+            self.net = tv.resnet34(pretrained=pretrained)
+        elif(num==50):
+            self.net = tv.resnet50(pretrained=pretrained)
+        elif(num==101):
+            self.net = tv.resnet101(pretrained=pretrained)
+        elif(num==152):
+            self.net = tv.resnet152(pretrained=pretrained)
+        self.N_slices = 5
+
+        self.conv1 = self.net.conv1
+        self.bn1 = self.net.bn1
+        self.relu = self.net.relu
+        self.maxpool = self.net.maxpool
+        self.layer1 = self.net.layer1
+        self.layer2 = self.net.layer2
+        self.layer3 = self.net.layer3
+        self.layer4 = self.net.layer4
+
+    def forward(self, X):
+        h = self.conv1(X)
+        h = self.bn1(h)
+        h = self.relu(h)
+        h_relu1 = h
+        h = self.maxpool(h)
+        h = self.layer1(h)
+        h_conv2 = h
+        h = self.layer2(h)
+        h_conv3 = h
+        h = self.layer3(h)
+        h_conv4 = h
+        h = self.layer4(h)
+        h_conv5 = h
+
+        outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5'])
+        out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
+
+        return out
diff --git a/examples/GANwithSelf-taughtLearning/GAN/lpips/weights/v0.0/alex.pth b/examples/GANwithSelf-taughtLearning/GAN/lpips/weights/v0.0/alex.pth
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diff --git a/examples/GANwithSelf-taughtLearning/GAN/lpips/weights/v0.1/squeeze.pth b/examples/GANwithSelf-taughtLearning/GAN/lpips/weights/v0.1/squeeze.pth
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diff --git a/examples/GANwithSelf-taughtLearning/GAN/lpips/weights/v0.1/vgg.pth b/examples/GANwithSelf-taughtLearning/GAN/lpips/weights/v0.1/vgg.pth
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diff --git a/examples/GANwithSelf-taughtLearning/GAN/models.py b/examples/GANwithSelf-taughtLearning/GAN/models.py
new file mode 100644
index 00000000..da1e5f93
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/models.py
@@ -0,0 +1,397 @@
+import torch
+import torch.nn as nn
+from torch.nn.utils import spectral_norm
+import torch.nn.functional as F
+
+import random
+
+seq = nn.Sequential
+
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        try:
+            m.weight.data.normal_(0.0, 0.02)
+        except:
+            pass
+    elif classname.find('BatchNorm') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+
+
+def conv2d(*args, **kwargs):
+    return spectral_norm(nn.Conv2d(*args, **kwargs))
+
+
+def convTranspose2d(*args, **kwargs):
+    return spectral_norm(nn.ConvTranspose2d(*args, **kwargs))
+
+
+def batchNorm2d(*args, **kwargs):
+    return nn.BatchNorm2d(*args, **kwargs)
+
+
+def linear(*args, **kwargs):
+    return spectral_norm(nn.Linear(*args, **kwargs))
+
+
+class PixelNorm(nn.Module):
+    def forward(self, input):
+        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
+
+
+class Reshape(nn.Module):
+    def __init__(self, shape):
+        super().__init__()
+        self.target_shape = shape
+
+    def forward(self, feat):
+        batch = feat.shape[0]
+        return feat.view(batch, *self.target_shape)
+
+
+class GLU(nn.Module):
+    def forward(self, x):
+        nc = x.size(1)
+        assert nc % 2 == 0, 'channels dont divide 2!'
+        nc = int(nc / 2)
+        return x[:, :nc] * torch.sigmoid(x[:, nc:])
+
+
+class NoiseInjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.weight = nn.Parameter(torch.zeros(1), requires_grad=True)
+
+    def forward(self, feat, noise=None):
+        if noise is None:
+            batch, _, height, width = feat.shape
+            noise = torch.randn(batch, 1, height, width).to(feat.device)
+
+        return feat + self.weight * noise
+
+
+class Swish(nn.Module):
+    def forward(self, feat):
+        return feat * torch.sigmoid(feat)
+
+
+class SEBlock(nn.Module):
+    def __init__(self, ch_in, ch_out):
+        super().__init__()
+
+        self.main = nn.Sequential(nn.AdaptiveAvgPool2d(4),
+                                  conv2d(ch_in, ch_out, 4, 1, 0, bias=False), Swish(),
+                                  conv2d(ch_out, ch_out, 1, 1, 0, bias=False), nn.Sigmoid())
+
+    def forward(self, feat_small, feat_big):
+        return feat_big * self.main(feat_small)
+
+
+class InitLayer(nn.Module):
+    def __init__(self, nz, channel):
+        super().__init__()
+
+        self.init = nn.Sequential(
+            convTranspose2d(nz, channel * 2, 4, 1, 0, bias=False),
+            batchNorm2d(channel * 2), GLU())
+
+    def forward(self, noise):
+        noise = noise.view(noise.shape[0], -1, 1, 1)
+        return self.init(noise)
+
+
+def UpBlock(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        conv2d(in_planes, out_planes * 2, 3, 1, 1, bias=False),
+        # convTranspose2d(in_planes, out_planes*2, 4, 2, 1, bias=False),
+        batchNorm2d(out_planes * 2), GLU())
+    return block
+
+
+def UpBlockComp(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        conv2d(in_planes, out_planes * 2, 3, 1, 1, bias=False),
+        # convTranspose2d(in_planes, out_planes*2, 4, 2, 1, bias=False),
+        NoiseInjection(),
+        batchNorm2d(out_planes * 2), GLU(),
+        conv2d(out_planes, out_planes * 2, 3, 1, 1, bias=False),
+        NoiseInjection(),
+        batchNorm2d(out_planes * 2), GLU()
+    )
+    return block
+
+
+class Generator(nn.Module):
+    def __init__(self, ngf=64, nz=100, nc=3, im_size=1024):
+        super(Generator, self).__init__()
+
+        nfc_multi = {4: 16, 8: 8, 16: 4, 32: 2, 64: 2, 128: 1, 256: 0.5, 512: 0.25, 1024: 0.125}
+        nfc = {}
+        for k, v in nfc_multi.items():
+            nfc[k] = int(v * ngf)
+
+        self.im_size = im_size
+
+        self.init = InitLayer(nz, channel=nfc[4])
+
+        self.feat_8 = UpBlockComp(nfc[4], nfc[8])
+        self.feat_16 = UpBlock(nfc[8], nfc[16])
+        self.feat_32 = UpBlockComp(nfc[16], nfc[32])
+        self.feat_64 = UpBlock(nfc[32], nfc[64])
+        self.feat_128 = UpBlockComp(nfc[64], nfc[128])
+        self.feat_256 = UpBlock(nfc[128], nfc[256])
+
+        self.se_64 = SEBlock(nfc[4], nfc[64])
+        self.se_128 = SEBlock(nfc[8], nfc[128])
+        self.se_256 = SEBlock(nfc[16], nfc[256])
+
+        self.to_128 = conv2d(nfc[128], nc, 1, 1, 0, bias=False)
+        self.to_big = conv2d(nfc[im_size], nc, 3, 1, 1, bias=False)
+
+        if im_size > 256:
+            self.feat_512 = UpBlockComp(nfc[256], nfc[512])
+            self.se_512 = SEBlock(nfc[32], nfc[512])
+        if im_size > 512:
+            self.feat_1024 = UpBlock(nfc[512], nfc[1024])
+
+    def forward(self, input):
+
+        feat_4 = self.init(input)
+        feat_8 = self.feat_8(feat_4)
+        feat_16 = self.feat_16(feat_8)
+        feat_32 = self.feat_32(feat_16)
+
+        feat_64 = self.se_64(feat_4, self.feat_64(feat_32))
+
+        feat_128 = self.se_128(feat_8, self.feat_128(feat_64))
+
+        feat_256 = self.se_256(feat_16, self.feat_256(feat_128))
+
+        if self.im_size == 256:
+            return [self.to_big(feat_256), self.to_128(feat_128)]
+
+        feat_512 = self.se_512(feat_32, self.feat_512(feat_256))
+        if self.im_size == 512:
+            return [self.to_big(feat_512), self.to_128(feat_128)]
+
+        feat_1024 = self.feat_1024(feat_512)
+
+        im_128 = torch.tanh(self.to_128(feat_128))
+        im_1024 = torch.tanh(self.to_big(feat_1024))
+
+        return [im_1024, im_128]
+
+
+class DownBlock(nn.Module):
+    def __init__(self, in_planes, out_planes):
+        super(DownBlock, self).__init__()
+
+        self.main = nn.Sequential(
+            conv2d(in_planes, out_planes, 4, 2, 1, bias=False),
+            batchNorm2d(out_planes), nn.LeakyReLU(0.2, inplace=True),
+        )
+
+    def forward(self, feat):
+        return self.main(feat)
+
+
+class DownBlockComp(nn.Module):
+    def __init__(self, in_planes, out_planes):
+        super(DownBlockComp, self).__init__()
+
+        self.main = nn.Sequential(
+            conv2d(in_planes, out_planes, 4, 2, 1, bias=False),
+            batchNorm2d(out_planes), nn.LeakyReLU(0.2, inplace=True),
+            conv2d(out_planes, out_planes, 3, 1, 1, bias=False),
+            batchNorm2d(out_planes), nn.LeakyReLU(0.2)
+        )
+
+        self.direct = nn.Sequential(
+            nn.AvgPool2d(2, 2),
+            conv2d(in_planes, out_planes, 1, 1, 0, bias=False),
+            batchNorm2d(out_planes), nn.LeakyReLU(0.2))
+
+    def forward(self, feat):
+        return (self.main(feat) + self.direct(feat)) / 2
+
+
+class Discriminator(nn.Module):
+    def __init__(self, ndf=64, nc=3, im_size=512):
+        super(Discriminator, self).__init__()
+        self.ndf = ndf
+        self.im_size = im_size
+
+        nfc_multi = {4: 16, 8: 16, 16: 8, 32: 4, 64: 2, 128: 1, 256: 0.5, 512: 0.25, 1024: 0.125}
+        nfc = {}
+        for k, v in nfc_multi.items():
+            nfc[k] = int(v * ndf)
+
+        if im_size == 1024:
+            self.down_from_big = nn.Sequential(
+                conv2d(nc, nfc[1024], 4, 2, 1, bias=False),
+                nn.LeakyReLU(0.2, inplace=True),
+                conv2d(nfc[1024], nfc[512], 4, 2, 1, bias=False),
+                batchNorm2d(nfc[512]),
+                nn.LeakyReLU(0.2, inplace=True))
+        elif im_size == 512:
+            self.down_from_big = nn.Sequential(
+                conv2d(nc, nfc[512], 4, 2, 1, bias=False),
+                nn.LeakyReLU(0.2, inplace=True))
+        elif im_size == 256:
+            self.down_from_big = nn.Sequential(
+                conv2d(nc, nfc[512], 3, 1, 1, bias=False),
+                nn.LeakyReLU(0.2, inplace=True))
+
+        self.down_4 = DownBlockComp(nfc[512], nfc[256])
+        self.down_8 = DownBlockComp(nfc[256], nfc[128])
+        self.down_16 = DownBlockComp(nfc[128], nfc[64])
+        self.down_32 = DownBlockComp(nfc[64], nfc[32])
+        self.down_64 = DownBlockComp(nfc[32], nfc[16])
+
+        self.rf_big = nn.Sequential(
+            conv2d(nfc[16], nfc[8], 1, 1, 0, bias=False),
+            batchNorm2d(nfc[8]), nn.LeakyReLU(0.2, inplace=True),
+            conv2d(nfc[8], 1, 4, 1, 0, bias=False))
+
+        self.se_2_16 = SEBlock(nfc[512], nfc[64])
+        self.se_4_32 = SEBlock(nfc[256], nfc[32])
+        self.se_8_64 = SEBlock(nfc[128], nfc[16])
+
+        self.down_from_small = nn.Sequential(
+            conv2d(nc, nfc[256], 4, 2, 1, bias=False),
+            nn.LeakyReLU(0.2, inplace=True),
+            DownBlock(nfc[256], nfc[128]),
+            DownBlock(nfc[128], nfc[64]),
+            DownBlock(nfc[64], nfc[32]), )
+
+        self.rf_small = conv2d(nfc[32], 1, 4, 1, 0, bias=False)
+
+        self.decoder_big = SimpleDecoder(nfc[16], nc)
+        self.decoder_part = SimpleDecoder(nfc[32], nc)
+        self.decoder_small = SimpleDecoder(nfc[32], nc)
+
+    def forward(self, imgs, label, part=None):
+        if type(imgs) is not list:
+            imgs = [F.interpolate(imgs, size=self.im_size), F.interpolate(imgs, size=128)]
+
+        feat_2 = self.down_from_big(imgs[0])
+        feat_4 = self.down_4(feat_2)
+        feat_8 = self.down_8(feat_4)
+
+        feat_16 = self.down_16(feat_8)
+        feat_16 = self.se_2_16(feat_2, feat_16)
+
+        feat_32 = self.down_32(feat_16)
+        feat_32 = self.se_4_32(feat_4, feat_32)
+
+        feat_last = self.down_64(feat_32)
+        feat_last = self.se_8_64(feat_8, feat_last)
+
+        # rf_0 = torch.cat([self.rf_big_1(feat_last).view(-1),self.rf_big_2(feat_last).view(-1)])
+        # rff_big = torch.sigmoid(self.rf_factor_big)
+        rf_0 = self.rf_big(feat_last).view(-1)
+
+        feat_small = self.down_from_small(imgs[1])
+        # rf_1 = torch.cat([self.rf_small_1(feat_small).view(-1),self.rf_small_2(feat_small).view(-1)])
+        rf_1 = self.rf_small(feat_small).view(-1)
+
+        if label == 'real':
+            rec_img_big = self.decoder_big(feat_last)
+            rec_img_small = self.decoder_small(feat_small)
+
+            assert part is not None
+            rec_img_part = None
+            if part == 0:
+                rec_img_part = self.decoder_part(feat_32[:, :, :8, :8])
+            if part == 1:
+                rec_img_part = self.decoder_part(feat_32[:, :, :8, 8:])
+            if part == 2:
+                rec_img_part = self.decoder_part(feat_32[:, :, 8:, :8])
+            if part == 3:
+                rec_img_part = self.decoder_part(feat_32[:, :, 8:, 8:])
+
+            return torch.cat([rf_0, rf_1]), [rec_img_big, rec_img_small, rec_img_part]
+
+        return torch.cat([rf_0, rf_1])
+
+
+class SimpleDecoder(nn.Module):
+    """docstring for CAN_SimpleDecoder"""
+
+    def __init__(self, nfc_in=64, nc=3):
+        super(SimpleDecoder, self).__init__()
+
+        nfc_multi = {4: 16, 8: 8, 16: 4, 32: 2, 64: 2, 128: 1, 256: 0.5, 512: 0.25, 1024: 0.125}
+        nfc = {}
+        for k, v in nfc_multi.items():
+            nfc[k] = int(v * 32)
+
+        def upBlock(in_planes, out_planes):
+            block = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='nearest'),
+                conv2d(in_planes, out_planes * 2, 3, 1, 1, bias=False),
+                batchNorm2d(out_planes * 2), GLU())
+            return block
+
+        self.main = nn.Sequential(nn.AdaptiveAvgPool2d(8),
+                                  upBlock(nfc_in, nfc[16]),
+                                  upBlock(nfc[16], nfc[32]),
+                                  upBlock(nfc[32], nfc[64]),
+                                  upBlock(nfc[64], nfc[128]),
+                                  conv2d(nfc[128], nc, 3, 1, 1, bias=False),
+                                  nn.Tanh())
+
+    def forward(self, input):
+        # input shape: c x 4 x 4
+        return self.main(input)
+
+
+from random import randint
+
+
+def random_crop(image, size):
+    h, w = image.shape[2:]
+    ch = randint(0, h - size - 1)
+    cw = randint(0, w - size - 1)
+    return image[:, :, ch:ch + size, cw:cw + size]
+
+
+class TextureDiscriminator(nn.Module):
+    def __init__(self, ndf=64, nc=3, im_size=512):
+        super(TextureDiscriminator, self).__init__()
+        self.ndf = ndf
+        self.im_size = im_size
+
+        nfc_multi = {4: 16, 8: 8, 16: 8, 32: 4, 64: 2, 128: 1, 256: 0.5, 512: 0.25, 1024: 0.125}
+        nfc = {}
+        for k, v in nfc_multi.items():
+            nfc[k] = int(v * ndf)
+
+        self.down_from_small = nn.Sequential(
+            conv2d(nc, nfc[256], 4, 2, 1, bias=False),
+            nn.LeakyReLU(0.2, inplace=True),
+            DownBlock(nfc[256], nfc[128]),
+            DownBlock(nfc[128], nfc[64]),
+            DownBlock(nfc[64], nfc[32]), )
+        self.rf_small = nn.Sequential(
+            conv2d(nfc[16], 1, 4, 1, 0, bias=False))
+
+        self.decoder_small = SimpleDecoder(nfc[32], nc)
+
+    def forward(self, img, label):
+        img = random_crop(img, size=128)
+
+        feat_small = self.down_from_small(img)
+        rf = self.rf_small(feat_small).view(-1)
+
+        if label == 'real':
+            rec_img_small = self.decoder_small(feat_small)
+
+            return rf, rec_img_small, img
+
+        return rf
\ No newline at end of file
diff --git a/examples/GANwithSelf-taughtLearning/GAN/operation.py b/examples/GANwithSelf-taughtLearning/GAN/operation.py
new file mode 100644
index 00000000..f61e46bb
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/operation.py
@@ -0,0 +1,137 @@
+import os
+import numpy as np
+import torch
+import torch.utils.data as data
+from torch.utils.data import Dataset
+from PIL import Image
+from copy import deepcopy
+import shutil
+import json
+
+def InfiniteSampler(n):
+    """Data sampler"""
+    i = n - 1
+    order = np.random.permutation(n)
+    while True:
+        yield order[i]
+        i += 1
+        if i >= n:
+            np.random.seed()
+            order = np.random.permutation(n)
+            i = 0
+
+
+class InfiniteSamplerWrapper(data.sampler.Sampler):
+    """Data sampler wrapper"""
+    def __init__(self, data_source):
+        self.num_samples = len(data_source)
+
+    def __iter__(self):
+        return iter(InfiniteSampler(self.num_samples))
+
+    def __len__(self):
+        return 2 ** 31
+
+
+def copy_G_params(model):
+    flatten = deepcopy(list(p.data for p in model.parameters()))
+    return flatten
+    
+
+def load_params(model, new_param):
+    for p, new_p in zip(model.parameters(), new_param):
+        p.data.copy_(new_p)
+
+
+def get_dir(args):
+    task_name = 'train_results/' + args['name']
+    saved_model_folder = os.path.join( task_name, 'models')
+    saved_image_folder = os.path.join( task_name, 'images')
+    
+    os.makedirs(saved_model_folder, exist_ok=True)
+    os.makedirs(saved_image_folder, exist_ok=True)
+
+    # for f in os.listdir('./'):
+    #     if '.py' in f:
+    #         shutil.copy(f, task_name+'/'+f)
+    
+    # with open( os.path.join(saved_model_folder, '../args.txt'), 'w') as f:
+    #     json.dump(args.__dict__, f, indent=2)
+
+    return saved_model_folder, saved_image_folder
+
+
+class  ImageFolder(Dataset):
+    """docstring for ArtDataset"""
+    def __init__(self, root, transform=None):
+        super( ImageFolder, self).__init__()
+        self.root = root
+
+        self.frame = self._parse_frame()
+        self.transform = transform
+
+    def _parse_frame(self):
+        frame = []
+        img_names = os.listdir(self.root)
+        img_names.sort()
+        for i in range(len(img_names)):
+            image_path = os.path.join(self.root, img_names[i])
+            if image_path[-4:] == '.jpg' or image_path[-4:] == '.png' or image_path[-5:] == '.jpeg': 
+                frame.append(image_path)
+        return frame
+
+    def __len__(self):
+        return len(self.frame)
+
+    def __getitem__(self, idx):
+        file = self.frame[idx]
+        img = Image.open(file).convert('RGB')
+            
+        if self.transform:
+            img = self.transform(img) 
+
+        return img
+
+
+
+from io import BytesIO
+import lmdb
+from torch.utils.data import Dataset
+
+
+class MultiResolutionDataset(Dataset):
+    def __init__(self, path, transform, resolution=256):
+        self.env = lmdb.open(
+            path,
+            max_readers=32,
+            readonly=True,
+            lock=False,
+            readahead=False,
+            meminit=False,
+        )
+
+        if not self.env:
+            raise IOError('Cannot open lmdb dataset', path)
+
+        with self.env.begin(write=False) as txn:
+            self.length = int(txn.get('length'.encode('utf-8')).decode('utf-8'))
+
+        self.resolution = resolution
+        self.transform = transform
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, index):
+        with self.env.begin(write=False) as txn:
+            key = f'{self.resolution}-{str(index).zfill(5)}'.encode('utf-8')
+            img_bytes = txn.get(key)
+            #key_asp = f'aspect_ratio-{str(index).zfill(5)}'.encode('utf-8')
+            #aspect_ratio = float(txn.get(key_asp).decode())
+
+        buffer = BytesIO(img_bytes)
+        img = Image.open(buffer)
+        img = self.transform(img)
+
+        return img
+
diff --git a/examples/GANwithSelf-taughtLearning/GAN/train.py b/examples/GANwithSelf-taughtLearning/GAN/train.py
new file mode 100644
index 00000000..2d3be08f
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/train.py
@@ -0,0 +1,220 @@
+import torch
+from torch import nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.utils.data.dataloader import DataLoader
+from torchvision import transforms
+from torchvision import utils as vutils
+
+import argparse
+import random
+from tqdm import tqdm
+
+import csv
+
+from models import weights_init, Discriminator, Generator
+from operation import copy_G_params, load_params, get_dir
+from operation import ImageFolder, InfiniteSamplerWrapper
+from diffaug import DiffAugment
+policy = 'color,translation'
+import lpips
+percept = lpips.PerceptualLoss(model='net-lin', net='vgg', use_gpu=True)
+
+from util import load_yaml
+
+def crop_image_by_part(image, part):
+    hw = image.shape[2] // 2
+    if part == 0:
+        return image[:, :, :hw, :hw]
+    if part == 1:
+        return image[:, :, :hw, hw:]
+    if part == 2:
+        return image[:, :, hw:, :hw]
+    if part == 3:
+        return image[:, :, hw:, hw:]
+
+
+def train_d(net, data, label="real"):
+    """Train function of discriminator"""
+    if label == "real":
+        part = random.randint(0, 3)
+        pred, [rec_all, rec_small, rec_part] = net(data, label, part=part)
+        err = F.relu(torch.rand_like(pred) * 0.2 + 0.8 - pred).mean() + \
+              percept(rec_all, F.interpolate(data, rec_all.shape[2])).sum() + \
+              percept(rec_small, F.interpolate(data, rec_small.shape[2])).sum() + \
+              percept(rec_part, F.interpolate(crop_image_by_part(data, part), rec_part.shape[2])).sum()
+        err.backward()
+        return pred.mean().item(), rec_all, rec_small, rec_part
+    else:
+        pred = net(data, label)
+        err = F.relu(torch.rand_like(pred) * 0.2 + 0.8 + pred).mean()
+        err.backward()
+        return pred.mean().item()
+
+
+def train(args):
+    data_root = args['path']
+    total_iterations = args['iter']
+    # checkpoint = args.ckpt
+    batch_size = args['batch_size']
+    im_size = args['im_size']
+    ndf = 64
+    ngf = 64
+    nz = 256
+    nlr = 0.0002
+    nbeta1 = 0.5
+    use_cuda = True
+    multi_gpu = False
+    dataloader_workers = 8
+    current_iteration = 0
+    save_interval = 100
+    saved_model_folder, saved_image_folder = get_dir(args)
+
+    device = torch.device("cpu")
+    if use_cuda:
+        device = torch.device("cuda:0")
+
+    transform_list = [
+        transforms.Resize((int(im_size), int(im_size))),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+    ]
+    trans = transforms.Compose(transform_list)
+
+    if 'lmdb' in data_root:
+        from operation import MultiResolutionDataset
+        dataset = MultiResolutionDataset(data_root, trans, 1024)
+    else:
+        dataset = ImageFolder(root=data_root, transform=trans)
+
+    dataloader = iter(DataLoader(dataset, batch_size=batch_size, shuffle=False,
+                                 sampler=InfiniteSamplerWrapper(dataset), num_workers=dataloader_workers,
+                                 pin_memory=True))
+    '''
+    loader = MultiEpochsDataLoader(dataset, batch_size=batch_size, 
+                               shuffle=True, num_workers=dataloader_workers, 
+                               pin_memory=True)
+    dataloader = CudaDataLoader(loader, 'cuda')
+    '''
+
+    # from model_s import Generator, Discriminator
+    netG = Generator(ngf=ngf, nz=nz, im_size=im_size)
+    netG.apply(weights_init)
+
+    netD = Discriminator(ndf=ndf, im_size=im_size)
+    netD.apply(weights_init)
+
+    netG.to(device)
+    netD.to(device)
+
+    avg_param_G = copy_G_params(netG)
+
+    fixed_noise = torch.FloatTensor(8, nz).normal_(0, 1).to(device)
+
+    optimizerG = optim.Adam(netG.parameters(), lr=nlr, betas=(nbeta1, 0.999))
+    optimizerD = optim.Adam(netD.parameters(), lr=nlr, betas=(nbeta1, 0.999))
+
+    # if checkpoint != 'None':
+    #     ckpt = torch.load(checkpoint)
+    #     netG.load_state_dict({k.replace('module.', ''): v for k, v in ckpt['g'].items()})
+    #     netD.load_state_dict({k.replace('module.', ''): v for k, v in ckpt['d'].items()})
+    #     avg_param_G = ckpt['g_ema']
+    #     optimizerG.load_state_dict(ckpt['opt_g'])
+    #     optimizerD.load_state_dict(ckpt['opt_d'])
+    #     current_iteration = int(checkpoint.split('_')[-1].split('.')[0])
+    #     del ckpt
+
+    if multi_gpu:
+        netG = nn.DataParallel(netG.to(device))
+        netD = nn.DataParallel(netD.to(device))
+
+    with open('train_cityscape.csv', 'w') as csvfile:
+        writer = csv.writer(csvfile)
+        writer.writerow(['epoch', 'd_loss', 'g_loss'])
+
+    for iteration in tqdm(range(current_iteration, total_iterations + 1)):
+        real_image = next(dataloader)
+        real_image = real_image.to(device)
+        current_batch_size = real_image.size(0)
+        noise = torch.Tensor(current_batch_size, nz).normal_(0, 1).to(device)
+
+        fake_images = netG(noise)
+
+        real_image = DiffAugment(real_image, policy=policy)
+        fake_images = [DiffAugment(fake, policy=policy) for fake in fake_images]
+
+        ## 2. train Discriminator
+        netD.zero_grad()
+
+        err_dr, rec_img_all, rec_img_small, rec_img_part = train_d(netD, real_image, label="real")
+        train_d(netD, [fi.detach() for fi in fake_images], label="fake")
+        optimizerD.step()
+
+        ## 3. train Generator
+        netG.zero_grad()
+        pred_g = netD(fake_images, "fake")
+        err_g = -pred_g.mean()
+
+        err_g.backward()
+        optimizerG.step()
+
+        for p, avg_p in zip(netG.parameters(), avg_param_G):
+            avg_p.mul_(0.999).add_(0.001 * p.data)
+
+        if iteration % 100 == 0:
+            print("GAN: loss d: %.5f    loss g: %.5f" % (err_dr, -err_g.item()))
+
+        if iteration % (save_interval * 10) == 0:
+            backup_para = copy_G_params(netG)
+            load_params(netG, avg_param_G)
+            with torch.no_grad():
+                vutils.save_image(netG(fixed_noise)[0].add(1).mul(0.5), saved_image_folder + '/%d.jpg' % iteration,
+                                  nrow=4)
+                vutils.save_image(torch.cat([
+                    F.interpolate(real_image, 128),
+                    rec_img_all, rec_img_small,
+                    rec_img_part]).add(1).mul(0.5), saved_image_folder + '/rec_%d.jpg' % iteration)
+            load_params(netG, backup_para)
+
+        if iteration % (save_interval * 50) == 0 or iteration == total_iterations:
+            backup_para = copy_G_params(netG)
+            load_params(netG, avg_param_G)
+            torch.save({'g': netG.state_dict(), 'd': netD.state_dict()}, saved_model_folder + '/%d.pth' % iteration)
+            load_params(netG, backup_para)
+            torch.save({'g': netG.state_dict(),
+                        'd': netD.state_dict(),
+                        'g_ema': avg_param_G,
+                        'opt_g': optimizerG.state_dict(),
+                        'opt_d': optimizerD.state_dict()}, saved_model_folder + '/all_%d.pth' % iteration)
+
+        with open('train_cityscape.csv', 'a') as csvfile:
+            writer = csv.writer(csvfile)
+            writer.writerow([iteration, err_dr, -err_g.item()])
+
+
+if __name__ == "__main__":
+    # parser = argparse.ArgumentParser(description='region gan')
+    #
+    # parser.add_argument('--path', type=str, default='../lmdbs/art_landscape_1k',
+    #                     help='path of resource dataset, should be a folder that has one or many sub image folders inside')
+    # parser.add_argument('--cuda', type=int, default=0, help='index of gpu to use')
+    # parser.add_argument('--name', type=str, default='test1', help='experiment name')
+    # parser.add_argument('--iter', type=int, default=50000, help='number of iterations')
+    # parser.add_argument('--start_iter', type=int, default=0, help='the iteration to start training')
+    # parser.add_argument('--batch_size', type=int, default=8, help='mini batch number of images')
+    # parser.add_argument('--im_size', type=int, default=1024, help='image resolution')
+    # parser.add_argument('--ckpt', type=str, default='None', help='checkpoint weight path if have one')
+
+    # args = parser.parse_args()
+    configs = load_yaml('../config.yaml')
+    print(configs)
+    args = dict()
+    args['path'] = configs['dataset_path']
+    args['iter'] = configs['GAN'][0]['iter']
+    args['batch_size'] = configs['GAN'][1]['batch_size']
+    args['im_size'] = configs['GAN'][2]['im_size']
+    args['name'] = configs['GAN'][3]['name']
+    print(args)
+    #
+    train(args)
diff --git a/examples/GANwithSelf-taughtLearning/GAN/train_cityscape.csv b/examples/GANwithSelf-taughtLearning/GAN/train_cityscape.csv
new file mode 100644
index 00000000..fe7aa9fd
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/GAN/train_cityscape.csv
@@ -0,0 +1,18 @@
+epoch,d_loss,g_loss
+0,0.20575577020645142,-0.3701064884662628
+1,0.1202360987663269,-0.382342666387558
+2,0.2591087818145752,-0.5365990996360779
+3,0.595060408115387,-0.6694589257240295
+4,0.4233413636684418,-0.45368048548698425
+5,0.7397832274436951,-0.513699471950531
+6,0.4850901961326599,-0.730381429195404
+7,0.6998284459114075,-0.7783517241477966
+8,0.7868878841400146,-0.8684926629066467
+9,0.6464230418205261,-0.9065934419631958
+10,0.6726473569869995,-0.8063123822212219
+11,0.6846868991851807,-0.7946229577064514
+12,0.6749208569526672,-0.8458480834960938
+13,0.7467646598815918,-0.9234620332717896
+14,1.1013246774673462,-1.1124314069747925
+15,0.5670914053916931,-0.8788895010948181
+16,0.814713716506958,-0.9621263146400452
diff --git a/examples/GANwithSelf-taughtLearning/README.md b/examples/GANwithSelf-taughtLearning/README.md
new file mode 100644
index 00000000..d3664859
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/README.md
@@ -0,0 +1,71 @@
+# Integrating GAN and Self-taught Learning into Ianvs Lifelong Learning
+
+## Overview
+
+We proposal an approach of combining [GAN](https://en.wikipedia.org/wiki/Generative_adversarial_network) and [Self-taught Learning](https://ai.stanford.edu/~hllee/icml07-selftaughtlearning.pdf) to solve small sample problem in the very stage of ianvs lifelong learning, as shown in the figure below.
+
+For quick start, jumping directly to [Developer Notes](##Developer Notes) is fine.
+
+![](./readme_img/ianvs-lifelonglearning.png)
+
+We describe the arichtecture and the process. More details can be seen in the [Architecture](##Architecture).
+
+![](./readme_img/ianvs-lifelonglearning2.png)
+
+1. Train GAN with orginal small sample data
+2. GAN generates more data according to the probability distribution
+3. Train Autoencoder (which is consist of encoder and decoder) with the data generated by GAN
+4. Use encoder to get feature representation of original small sample data
+5. Use feature representation and orignal labels to train model that the user needs
+6. Ouput a well trained model
+
+## Architecture
+
+- Overview
+
+  ![](./readme_img/overview.png)
+
+- GAN (We refer to [Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis](https://openreview.net/forum?id=1Fqg133qRaI).)
+
+  Discriminator
+
+  ![](./readme_img/discriminator.png)
+
+  Generator
+
+  ![](./readme_img/generator.png)
+
+- Convolutional AutoEncoder of Self-taught Learning
+
+  ![](./readme_img/cae.png)
+
+## Developer Notes
+
+```bash
+GANwithSelf-taughtLearning # root path of the project
+	- config.yaml # the config of input path (dataset and model to be trained) as well as hyperparameter of GAN and Self-taught Learning
+	- GAN # GAN module
+		- models.py # define discriminator and generator
+		- train.py # train GAN here
+		- ./train_results # training outputs, like GAN model, training loss and evaluation of GAN
+	- self-taught-learning # self-taught learning module
+		- models.py # Define AutoEncoder. Here we use Convolutional AutoEncoder (CAE).
+		- train.py # train GAN here
+		- ./train_results # training outputs, like encoder model and training loss
+	- model-to-be-trained # model to be trained module
+		- train.py # train model
+		- model.py # define model
+		- ./train_results # training results of the model to be trained
+	- util.py # util module
+```
+
+To use, config the `config.yaml` to let `GANwithSelf-taughtLearning` know where the **dataset** is, what **model** you want to train and the **hyperparameters**.
+
+A common use process can be shown below:
+
+1. run `./GAN/train.py`
+2. run `./self-taught-learning/train.py`
+3. run `./train.py`
+
+
+
diff --git a/examples/GANwithSelf-taughtLearning/config.yaml b/examples/GANwithSelf-taughtLearning/config.yaml
new file mode 100644
index 00000000..8a7fcbf5
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/config.yaml
@@ -0,0 +1,26 @@
+dataset_path: '../data/img'
+
+# GAN config
+GAN:
+  - iter: 50000
+  - batch_size: 8
+  - im_size: 1024
+  - name: 'test2'
+
+# Self-taught Learning config
+STL:
+  - iter: 100
+  - lr: 1.0e-3
+  - batch_size: 30
+  - name: 'encoder_models'
+
+# model to be trained config
+# here we take deeplabv3 as example
+deeplabv3:
+  - iter: 1000
+  - batch_size: 3
+  - lr: 1.0e-4
+  - name: "1"
+  - cityscapes_data_path: "/home/nailtu/data/cityscapes"
+  - cityscapes_meta_path: '/home/nailtu/data/cityscapes/meta'
+  - class_weights: '/home/nailtu/data/cityscapes/meta/class_weights.pkl'
\ No newline at end of file
diff --git a/examples/GANwithSelf-taughtLearning/deeplabv3/__init__.py b/examples/GANwithSelf-taughtLearning/deeplabv3/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/GANwithSelf-taughtLearning/deeplabv3/datasets.py b/examples/GANwithSelf-taughtLearning/deeplabv3/datasets.py
new file mode 100644
index 00000000..196eed16
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/deeplabv3/datasets.py
@@ -0,0 +1,146 @@
+import torch
+import torch.utils.data
+
+import numpy as np
+import cv2
+import os
+
+train_dirs = ["jena/", "zurich/", "weimar/", "ulm/", "tubingen/", "stuttgart/",
+              "strasbourg/", "monchengladbach/", "krefeld/", "hanover/",
+              "hamburg/", "erfurt/", "dusseldorf/", "darmstadt/", "cologne/",
+              "bremen/", "bochum/", "aachen/"]
+val_dirs = ["frankfurt/", "munster/", "lindau/"]
+test_dirs = ["berlin", "bielefeld", "bonn", "leverkusen", "mainz", "munich"]
+
+class DatasetTrain(torch.utils.data.Dataset):
+    def __init__(self, cityscapes_data_path, cityscapes_meta_path):
+        self.img_dir = cityscapes_data_path + "/leftImg8bit/train/"
+        self.label_dir = cityscapes_meta_path + "/label_imgs/"
+
+        self.img_h = 1024
+        self.img_w = 2048
+
+        self.new_img_h = 512
+        self.new_img_w = 1024
+
+        self.examples = []
+        for train_dir in train_dirs:
+            train_img_dir_path = self.img_dir + train_dir
+
+            file_names = os.listdir(train_img_dir_path)
+            for file_name in file_names:
+                img_id = file_name.split("_leftImg8bit.png")[0]
+
+                img_path = train_img_dir_path + file_name
+
+                label_img_path = self.label_dir + img_id + ".png"
+
+                example = {}
+                example["img_path"] = img_path
+                example["label_img_path"] = label_img_path
+                example["img_id"] = img_id
+                self.examples.append(example)
+        self.examples = self.examples[0:99]
+        self.num_examples = len(self.examples)
+
+    def __getitem__(self, index):
+        example = self.examples[index]
+
+        img_path = example["img_path"]
+        img = cv2.imread(img_path, -1)
+        # img = cv2.resize(img, (self.new_img_w, self.new_img_h),
+        #                  interpolation=cv2.INTER_NEAREST)
+        label_img_path = example["label_img_path"]
+        label_img = cv2.imread(label_img_path, -1)
+        label_img = cv2.resize(label_img, (self.new_img_w, self.new_img_h),
+                               interpolation=cv2.INTER_NEAREST)
+        # flip = np.random.randint(low=0, high=2)
+        # if flip == 1:
+        #     img = cv2.flip(img, 1)
+        #     label_img = cv2.flip(label_img, 1)
+        # scale = np.random.uniform(low=0.7, high=2.0)
+        # new_img_h = int(scale*self.new_img_h)
+        # new_img_w = int(scale*self.new_img_w)
+        # img = cv2.resize(img, (new_img_w, new_img_h),
+        #                  interpolation=cv2.INTER_NEAREST)
+        # label_img = cv2.resize(label_img, (new_img_w, new_img_h),
+        #                        interpolation=cv2.INTER_NEAREST)
+        # start_x = np.random.randint(low=0, high=(new_img_w - 256))
+        # end_x = start_x + 256
+        # start_y = np.random.randint(low=0, high=(new_img_h - 256))
+        # end_y = start_y + 256
+        #
+        # img = img[start_y:end_y, start_x:end_x]
+        # label_img = label_img[start_y:end_y, start_x:end_x]
+        img = img/255.0
+        img = img - np.array([0.485, 0.456, 0.406])
+        img = img/np.array([0.229, 0.224, 0.225])
+        img = np.transpose(img, (2, 0, 1))
+        img = img.astype(np.float32)
+        img = torch.from_numpy(img)
+        label_img = torch.from_numpy(label_img)
+
+        return (img, label_img)
+
+    def __len__(self):
+        return self.num_examples
+
+class DatasetVal(torch.utils.data.Dataset):
+    def __init__(self, cityscapes_data_path, cityscapes_meta_path):
+        self.img_dir = cityscapes_data_path + "/leftImg8bit/val/"
+        self.label_dir = cityscapes_meta_path + "/label_imgs/"
+
+        self.img_h = 1024
+        self.img_w = 2048
+
+        self.new_img_h = 1024
+        self.new_img_w = 2048
+
+        self.examples = []
+        for val_dir in val_dirs:
+            val_img_dir_path = self.img_dir + val_dir
+
+            file_names = os.listdir(val_img_dir_path)
+            for file_name in file_names:
+                img_id = file_name.split("_leftImg8bit.png")[0]
+
+                img_path = val_img_dir_path + file_name
+
+                label_img_path = self.label_dir + img_id + ".png"
+                label_img = cv2.imread(label_img_path, -1)
+
+                example = {}
+                example["img_path"] = img_path
+                example["label_img_path"] = label_img_path
+                example["img_id"] = img_id
+                self.examples.append(example)
+        self.examples = self.examples[0:99]
+        self.num_examples = len(self.examples)
+
+    def __getitem__(self, index):
+        example = self.examples[index]
+
+        img_id = example["img_id"]
+
+        img_path = example["img_path"]
+        img = cv2.imread(img_path, -1)
+        img = cv2.resize(img, (self.new_img_w, self.new_img_h),
+                         interpolation=cv2.INTER_NEAREST)
+
+        label_img_path = example["label_img_path"]
+        label_img = cv2.imread(label_img_path, -1)
+        label_img = cv2.resize(label_img, (self.new_img_w, self.new_img_h),
+                               interpolation=cv2.INTER_NEAREST)
+        img = img/255.0
+        img = img - np.array([0.485, 0.456, 0.406])
+        img = img/np.array([0.229, 0.224, 0.225])
+        img = np.transpose(img, (2, 0, 1))
+        img = img.astype(np.float32)
+
+        img = torch.from_numpy(img)
+        label_img = torch.from_numpy(label_img)
+
+        return (img, label_img, img_id)
+
+    def __len__(self):
+        return self.num_examples
\ No newline at end of file
diff --git a/examples/GANwithSelf-taughtLearning/deeplabv3/model/aspp.py b/examples/GANwithSelf-taughtLearning/deeplabv3/model/aspp.py
new file mode 100644
index 00000000..ccb49b88
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/deeplabv3/model/aspp.py
@@ -0,0 +1,99 @@
+# camera-ready
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ASPP(nn.Module):
+    def __init__(self, num_classes):
+        super(ASPP, self).__init__()
+
+        self.conv_1x1_1 = nn.Conv2d(512, 256, kernel_size=1)
+        self.bn_conv_1x1_1 = nn.BatchNorm2d(256)
+
+        self.conv_3x3_1 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=6, dilation=6)
+        self.bn_conv_3x3_1 = nn.BatchNorm2d(256)
+
+        self.conv_3x3_2 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=12, dilation=12)
+        self.bn_conv_3x3_2 = nn.BatchNorm2d(256)
+
+        self.conv_3x3_3 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=18, dilation=18)
+        self.bn_conv_3x3_3 = nn.BatchNorm2d(256)
+
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+
+        self.conv_1x1_2 = nn.Conv2d(512, 256, kernel_size=1)
+        self.bn_conv_1x1_2 = nn.BatchNorm2d(256)
+
+        self.conv_1x1_3 = nn.Conv2d(1280, 256, kernel_size=1) # (1280 = 5*256)
+        self.bn_conv_1x1_3 = nn.BatchNorm2d(256)
+
+        self.conv_1x1_4 = nn.Conv2d(256, num_classes, kernel_size=1)
+
+    def forward(self, feature_map):
+        # (feature_map has shape (batch_size, 512, h/16, w/16)) (assuming self.resnet is ResNet18_OS16 or ResNet34_OS16. If self.resnet instead is ResNet18_OS8 or ResNet34_OS8, it will be (batch_size, 512, h/8, w/8))
+
+        feature_map_h = feature_map.size()[2] # (== h/16)
+        feature_map_w = feature_map.size()[3] # (== w/16)
+
+        out_1x1 = F.relu(self.bn_conv_1x1_1(self.conv_1x1_1(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
+        out_3x3_1 = F.relu(self.bn_conv_3x3_1(self.conv_3x3_1(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
+        out_3x3_2 = F.relu(self.bn_conv_3x3_2(self.conv_3x3_2(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
+        out_3x3_3 = F.relu(self.bn_conv_3x3_3(self.conv_3x3_3(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
+
+        out_img = self.avg_pool(feature_map) # (shape: (batch_size, 512, 1, 1))
+        out_img = F.relu(self.bn_conv_1x1_2(self.conv_1x1_2(out_img))) # (shape: (batch_size, 256, 1, 1))
+        out_img = F.upsample(out_img, size=(feature_map_h, feature_map_w), mode="bilinear") # (shape: (batch_size, 256, h/16, w/16))
+
+        out = torch.cat([out_1x1, out_3x3_1, out_3x3_2, out_3x3_3, out_img], 1) # (shape: (batch_size, 1280, h/16, w/16))
+        out = F.relu(self.bn_conv_1x1_3(self.conv_1x1_3(out))) # (shape: (batch_size, 256, h/16, w/16))
+        out = self.conv_1x1_4(out) # (shape: (batch_size, num_classes, h/16, w/16))
+
+        return out
+
+class ASPP_Bottleneck(nn.Module):
+    def __init__(self, num_classes):
+        super(ASPP_Bottleneck, self).__init__()
+
+        self.conv_1x1_1 = nn.Conv2d(4*512, 256, kernel_size=1)
+        self.bn_conv_1x1_1 = nn.BatchNorm2d(256)
+
+        self.conv_3x3_1 = nn.Conv2d(4*512, 256, kernel_size=3, stride=1, padding=6, dilation=6)
+        self.bn_conv_3x3_1 = nn.BatchNorm2d(256)
+
+        self.conv_3x3_2 = nn.Conv2d(4*512, 256, kernel_size=3, stride=1, padding=12, dilation=12)
+        self.bn_conv_3x3_2 = nn.BatchNorm2d(256)
+
+        self.conv_3x3_3 = nn.Conv2d(4*512, 256, kernel_size=3, stride=1, padding=18, dilation=18)
+        self.bn_conv_3x3_3 = nn.BatchNorm2d(256)
+
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+
+        self.conv_1x1_2 = nn.Conv2d(4*512, 256, kernel_size=1)
+        self.bn_conv_1x1_2 = nn.BatchNorm2d(256)
+
+        self.conv_1x1_3 = nn.Conv2d(1280, 256, kernel_size=1) # (1280 = 5*256)
+        self.bn_conv_1x1_3 = nn.BatchNorm2d(256)
+
+        self.conv_1x1_4 = nn.Conv2d(256, num_classes, kernel_size=1)
+
+    def forward(self, feature_map):
+        # (feature_map has shape (batch_size, 4*512, h/16, w/16))
+
+        feature_map_h = feature_map.size()[2] # (== h/16)
+        feature_map_w = feature_map.size()[3] # (== w/16)
+
+        out_1x1 = F.relu(self.bn_conv_1x1_1(self.conv_1x1_1(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
+        out_3x3_1 = F.relu(self.bn_conv_3x3_1(self.conv_3x3_1(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
+        out_3x3_2 = F.relu(self.bn_conv_3x3_2(self.conv_3x3_2(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
+        out_3x3_3 = F.relu(self.bn_conv_3x3_3(self.conv_3x3_3(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
+
+        out_img = self.avg_pool(feature_map) # (shape: (batch_size, 512, 1, 1))
+        out_img = F.relu(self.bn_conv_1x1_2(self.conv_1x1_2(out_img))) # (shape: (batch_size, 256, 1, 1))
+        out_img = F.upsample(out_img, size=(feature_map_h, feature_map_w), mode="bilinear") # (shape: (batch_size, 256, h/16, w/16))
+
+        out = torch.cat([out_1x1, out_3x3_1, out_3x3_2, out_3x3_3, out_img], 1) # (shape: (batch_size, 1280, h/16, w/16))
+        out = F.relu(self.bn_conv_1x1_3(self.conv_1x1_3(out))) # (shape: (batch_size, 256, h/16, w/16))
+        out = self.conv_1x1_4(out) # (shape: (batch_size, num_classes, h/16, w/16))
+
+        return out
diff --git a/examples/GANwithSelf-taughtLearning/deeplabv3/model/deeplabv3.py b/examples/GANwithSelf-taughtLearning/deeplabv3/model/deeplabv3.py
new file mode 100644
index 00000000..a97107e9
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/deeplabv3/model/deeplabv3.py
@@ -0,0 +1,46 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import os
+import sys
+sys.path.append('model')
+from resnet import ResNet18_OS16, ResNet34_OS16, ResNet50_OS16, ResNet101_OS16, ResNet152_OS16, ResNet18_OS8, ResNet34_OS8
+from aspp import ASPP, ASPP_Bottleneck
+
+class DeepLabV3(nn.Module):
+    def __init__(self, model_id, project_dir):
+        super(DeepLabV3, self).__init__()
+
+        self.num_classes = 20
+
+        self.model_id = model_id
+        self.project_dir = project_dir
+        self.create_model_dirs()
+
+        self.resnet = ResNet18_OS8() # NOTE! specify the type of ResNet here
+        self.aspp = ASPP(num_classes=self.num_classes) # NOTE! if you use ResNet50-152, set self.aspp = ASPP_Bottleneck(num_classes=self.num_classes) instead
+
+    def forward(self, x):
+        # (x has shape (batch_size, 3, h, w))
+
+        h = x.size()[2]
+        w = x.size()[3]
+
+        feature_map = self.resnet(x) # (shape: (batch_size, 512, h/16, w/16)) (assuming self.resnet is ResNet18_OS16 or ResNet34_OS16. If self.resnet is ResNet18_OS8 or ResNet34_OS8, it will be (batch_size, 512, h/8, w/8). If self.resnet is ResNet50-152, it will be (batch_size, 4*512, h/16, w/16))
+        # print(feature_map.shape)
+        output = self.aspp(feature_map) # (shape: (batch_size, num_classes, h/16, w/16))
+
+        output = F.upsample(output, size=(h, w), mode="bilinear") # (shape: (batch_size, num_classes, h, w))
+
+        return output
+
+    def create_model_dirs(self):
+        self.logs_dir = self.project_dir + "/training_logs"
+        self.model_dir = self.logs_dir + "/model_%s" % self.model_id
+        self.checkpoints_dir = self.model_dir + "/checkpoints"
+        if not os.path.exists(self.logs_dir):
+            os.makedirs(self.logs_dir)
+        if not os.path.exists(self.model_dir):
+            os.makedirs(self.model_dir)
+            os.makedirs(self.checkpoints_dir)
diff --git a/examples/GANwithSelf-taughtLearning/deeplabv3/model/resnet.py b/examples/GANwithSelf-taughtLearning/deeplabv3/model/resnet.py
new file mode 100644
index 00000000..80d2dacd
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/deeplabv3/model/resnet.py
@@ -0,0 +1,233 @@
+# camera-ready
+
+# NOTE! OS: output stride, the ratio of input image resolution to final output resolution (OS16: output size is (img_h/16, img_w/16)) (OS8: output size is (img_h/8, img_w/8))
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models as models
+
+def make_layer(block, in_channels, channels, num_blocks, stride=1, dilation=1):
+    strides = [stride] + [1]*(num_blocks - 1) # (stride == 2, num_blocks == 4 --> strides == [2, 1, 1, 1])
+
+    blocks = []
+    for stride in strides:
+        blocks.append(block(in_channels=in_channels, channels=channels, stride=stride, dilation=dilation))
+        in_channels = block.expansion*channels
+
+    layer = nn.Sequential(*blocks) # (*blocks: call with unpacked list entires as arguments)
+
+    return layer
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_channels, channels, stride=1, dilation=1):
+        super(BasicBlock, self).__init__()
+
+        out_channels = self.expansion*channels
+
+        self.conv1 = nn.Conv2d(in_channels, channels, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, bias=False)
+        self.bn1 = nn.BatchNorm2d(channels)
+
+        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=dilation, dilation=dilation, bias=False)
+        self.bn2 = nn.BatchNorm2d(channels)
+
+        if (stride != 1) or (in_channels != out_channels):
+            conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
+            bn = nn.BatchNorm2d(out_channels)
+            self.downsample = nn.Sequential(conv, bn)
+        else:
+            self.downsample = nn.Sequential()
+
+    def forward(self, x):
+        # (x has shape: (batch_size, in_channels, h, w))
+
+        out = F.relu(self.bn1(self.conv1(x))) # (shape: (batch_size, channels, h, w) if stride == 1, (batch_size, channels, h/2, w/2) if stride == 2)
+        out = self.bn2(self.conv2(out)) # (shape: (batch_size, channels, h, w) if stride == 1, (batch_size, channels, h/2, w/2) if stride == 2)
+
+        out = out + self.downsample(x) # (shape: (batch_size, channels, h, w) if stride == 1, (batch_size, channels, h/2, w/2) if stride == 2)
+
+        out = F.relu(out) # (shape: (batch_size, channels, h, w) if stride == 1, (batch_size, channels, h/2, w/2) if stride == 2)
+
+        return out
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_channels, channels, stride=1, dilation=1):
+        super(Bottleneck, self).__init__()
+
+        out_channels = self.expansion*channels
+
+        self.conv1 = nn.Conv2d(in_channels, channels, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(channels)
+
+        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, bias=False)
+        self.bn2 = nn.BatchNorm2d(channels)
+
+        self.conv3 = nn.Conv2d(channels, out_channels, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(out_channels)
+
+        if (stride != 1) or (in_channels != out_channels):
+            conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
+            bn = nn.BatchNorm2d(out_channels)
+            self.downsample = nn.Sequential(conv, bn)
+        else:
+            self.downsample = nn.Sequential()
+
+    def forward(self, x):
+        # (x has shape: (batch_size, in_channels, h, w))
+
+        out = F.relu(self.bn1(self.conv1(x))) # (shape: (batch_size, channels, h, w))
+        out = F.relu(self.bn2(self.conv2(out))) # (shape: (batch_size, channels, h, w) if stride == 1, (batch_size, channels, h/2, w/2) if stride == 2)
+        out = self.bn3(self.conv3(out)) # (shape: (batch_size, out_channels, h, w) if stride == 1, (batch_size, out_channels, h/2, w/2) if stride == 2)
+
+        out = out + self.downsample(x) # (shape: (batch_size, out_channels, h, w) if stride == 1, (batch_size, out_channels, h/2, w/2) if stride == 2)
+
+        out = F.relu(out) # (shape: (batch_size, out_channels, h, w) if stride == 1, (batch_size, out_channels, h/2, w/2) if stride == 2)
+
+        return out
+
+class ResNet_Bottleneck_OS16(nn.Module):
+    def __init__(self, num_layers):
+        super(ResNet_Bottleneck_OS16, self).__init__()
+
+        if num_layers == 50:
+            resnet = models.resnet50()
+            # load pretrained model:
+            resnet.load_state_dict(torch.load("/root/deeplabv3/pretrained_models/resnet/resnet50-19c8e357.pth"))
+            # remove fully connected layer, avg pool and layer5:
+            self.resnet = nn.Sequential(*list(resnet.children())[:-3])
+
+            print ("pretrained resnet, 50")
+        elif num_layers == 101:
+            resnet = models.resnet101()
+            # load pretrained model:
+            resnet.load_state_dict(torch.load("/root/deeplabv3/pretrained_models/resnet/resnet101-5d3b4d8f.pth"))
+            # remove fully connected layer, avg pool and layer5:
+            self.resnet = nn.Sequential(*list(resnet.children())[:-3])
+
+            print ("pretrained resnet, 101")
+        elif num_layers == 152:
+            resnet = models.resnet152()
+            # load pretrained model:
+            resnet.load_state_dict(torch.load("/root/deeplabv3/pretrained_models/resnet/resnet152-b121ed2d.pth"))
+            # remove fully connected layer, avg pool and layer5:
+            self.resnet = nn.Sequential(*list(resnet.children())[:-3])
+
+            print ("pretrained resnet, 152")
+        else:
+            raise Exception("num_layers must be in {50, 101, 152}!")
+
+        self.layer5 = make_layer(Bottleneck, in_channels=4*256, channels=512, num_blocks=3, stride=1, dilation=2)
+
+    def forward(self, x):
+        # (x has shape (batch_size, 3, h, w))
+
+        # pass x through (parts of) the pretrained ResNet:
+        c4 = self.resnet(x) # (shape: (batch_size, 4*256, h/16, w/16)) (it's called c4 since 16 == 2^4)
+
+        output = self.layer5(c4) # (shape: (batch_size, 4*512, h/16, w/16))
+
+        return output
+
+class ResNet_BasicBlock_OS16(nn.Module):
+    def __init__(self, num_layers):
+        super(ResNet_BasicBlock_OS16, self).__init__()
+
+        if num_layers == 18:
+            resnet = models.resnet18()
+            # load pretrained model:
+            resnet.load_state_dict(torch.load("/root/deeplabv3/pretrained_models/resnet/resnet18-5c106cde.pth"))
+            # remove fully connected layer, avg pool and layer5:
+            self.resnet = nn.Sequential(*list(resnet.children())[:-3])
+
+            num_blocks = 2
+            print ("pretrained resnet, 18")
+        elif num_layers == 34:
+            resnet = models.resnet34()
+            # load pretrained model:
+            resnet.load_state_dict(torch.load("/root/deeplabv3/pretrained_models/resnet/resnet34-333f7ec4.pth"))
+            # remove fully connected layer, avg pool and layer5:
+            self.resnet = nn.Sequential(*list(resnet.children())[:-3])
+
+            num_blocks = 3
+            print ("pretrained resnet, 34")
+        else:
+            raise Exception("num_layers must be in {18, 34}!")
+
+        self.layer5 = make_layer(BasicBlock, in_channels=256, channels=512, num_blocks=num_blocks, stride=1, dilation=2)
+
+    def forward(self, x):
+        # (x has shape (batch_size, 3, h, w))
+
+        # pass x through (parts of) the pretrained ResNet:
+        c4 = self.resnet(x) # (shape: (batch_size, 256, h/16, w/16)) (it's called c4 since 16 == 2^4)
+
+        output = self.layer5(c4) # (shape: (batch_size, 512, h/16, w/16))
+
+        return output
+
+class ResNet_BasicBlock_OS8(nn.Module):
+    def __init__(self, num_layers):
+        super(ResNet_BasicBlock_OS8, self).__init__()
+
+        if num_layers == 18:
+            resnet = models.resnet18()
+            # load pretrained model:
+            resnet.load_state_dict(torch.load("/home/nailtu/PycharmProjects/deeplabv3-master/pretrained_models/resnet/resnet18-5c106cde.pth"))
+            # remove fully connected layer, avg pool, layer4 and layer5:
+            self.resnet = nn.Sequential(*list(resnet.children())[:-4])
+
+            num_blocks_layer_4 = 2
+            num_blocks_layer_5 = 2
+            print ("pretrained resnet, 18")
+        elif num_layers == 34:
+            resnet = models.resnet34()
+            # load pretrained model:
+            resnet.load_state_dict(torch.load("/root/deeplabv3/pretrained_models/resnet/resnet34-333f7ec4.pth"))
+            # remove fully connected layer, avg pool, layer4 and layer5:
+            self.resnet = nn.Sequential(*list(resnet.children())[:-4])
+
+            num_blocks_layer_4 = 6
+            num_blocks_layer_5 = 3
+            print ("pretrained resnet, 34")
+        else:
+            raise Exception("num_layers must be in {18, 34}!")
+
+        self.layer4 = make_layer(BasicBlock, in_channels=128, channels=256, num_blocks=num_blocks_layer_4, stride=1, dilation=2)
+
+        self.layer5 = make_layer(BasicBlock, in_channels=256, channels=512, num_blocks=num_blocks_layer_5, stride=1, dilation=4)
+
+    def forward(self, x):
+        # (x has shape (batch_size, 3, h, w))
+
+        # pass x through (parts of) the pretrained ResNet:
+        c3 = self.resnet(x) # (shape: (batch_size, 128, h/8, w/8)) (it's called c3 since 8 == 2^3)
+
+        output = self.layer4(c3) # (shape: (batch_size, 256, h/8, w/8))
+        output = self.layer5(output) # (shape: (batch_size, 512, h/8, w/8))
+
+        return output
+
+def ResNet18_OS16():
+    return ResNet_BasicBlock_OS16(num_layers=18)
+
+def ResNet34_OS16():
+    return ResNet_BasicBlock_OS16(num_layers=34)
+
+def ResNet50_OS16():
+    return ResNet_Bottleneck_OS16(num_layers=50)
+
+def ResNet101_OS16():
+    return ResNet_Bottleneck_OS16(num_layers=101)
+
+def ResNet152_OS16():
+    return ResNet_Bottleneck_OS16(num_layers=152)
+
+def ResNet18_OS8():
+    return ResNet_BasicBlock_OS8(num_layers=18)
+
+def ResNet34_OS8():
+    return ResNet_BasicBlock_OS8(num_layers=34)
diff --git a/examples/GANwithSelf-taughtLearning/deeplabv3/train.py b/examples/GANwithSelf-taughtLearning/deeplabv3/train.py
new file mode 100644
index 00000000..c7ac21ac
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/deeplabv3/train.py
@@ -0,0 +1,147 @@
+import os
+
+from datasets import DatasetTrain, DatasetVal
+from model.deeplabv3 import DeepLabV3
+import sys
+from utils.utils import add_weight_decay
+
+import torch
+import torch.utils.data
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.optim as optim
+import torch.nn.functional as F
+
+import numpy as np
+import pickle
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+from util import load_yaml
+
+class Encoder(nn.Module):
+    def __init__(self) -> None:
+        super(Encoder, self).__init__()
+        self.enc3 = nn.Conv2d(
+            in_channels=3, out_channels=8, kernel_size=3, stride=2, padding=1
+        )
+        self.enc4 = nn.Conv2d(
+            in_channels=8, out_channels=3, kernel_size=3, stride=1, padding=1
+        )
+
+    def forward(self, x):
+        x = F.relu(self.enc3(x))
+        x = F.relu(self.enc4(x))
+        return x
+
+
+if __name__ == '__main__':
+    configs = load_yaml('../config.yaml')
+    model_id = configs['deeplabv3'][3]['name']
+
+    encoder = Encoder().cuda()
+    encoder.load_state_dict(torch.load('../self-taught-learning/train_results/encoder_models4/encoder50.pth'))
+
+    num_epochs = configs['deeplabv3'][0]['iter']
+    batch_size = configs['deeplabv3'][1]['batch_size']
+    learning_rate = configs['deeplabv3'][2]['lr']
+
+    network = DeepLabV3(model_id, project_dir=os.getcwd()).cuda()
+
+    train_dataset = DatasetTrain(cityscapes_data_path=configs['deeplabv3'][4]['cityscapes_data_path'],
+                                 cityscapes_meta_path=configs['deeplabv3'][5]['cityscapes_meta_path'])
+    val_dataset = DatasetVal(cityscapes_data_path=configs['deeplabv3'][4]['cityscapes_data_path'],
+                             cityscapes_meta_path=configs['deeplabv3'][5]['cityscapes_meta_path'])
+
+    num_train_batches = int(len(train_dataset)/batch_size)
+    num_val_batches = int(len(val_dataset)/batch_size)
+    print ("num_train_batches:", num_train_batches)
+    print ("num_val_batches:", num_val_batches)
+
+    train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                               batch_size=batch_size, shuffle=True,
+                                               num_workers=1)
+    val_loader = torch.utils.data.DataLoader(dataset=val_dataset,
+                                             batch_size=batch_size, shuffle=False,
+                                             num_workers=1)
+
+    params = add_weight_decay(network, l2_value=0.0001)
+    optimizer = torch.optim.Adam(params, lr=learning_rate)
+
+    with open(configs['deeplabv3'][6]['class_weights'], "rb") as file:
+        class_weights = np.array(pickle.load(file))
+    class_weights = torch.from_numpy(class_weights)
+    class_weights = Variable(class_weights.type(torch.FloatTensor)).cuda()
+
+    loss_fn = nn.CrossEntropyLoss(weight=class_weights)
+
+    epoch_losses_train = []
+    epoch_losses_val = []
+    for epoch in range(num_epochs):
+        print ("###########################")
+        print ("######## NEW EPOCH ########")
+        print ("###########################")
+        print ("epoch: %d/%d" % (epoch+1, num_epochs))
+
+        ############################################################################
+        # train:
+        ############################################################################
+        network.train() # (set in training mode, this affects BatchNorm and dropout)
+        batch_losses = []
+        for step, (imgs, label_imgs) in enumerate(train_loader):
+            imgs = Variable(imgs).cuda()
+            imgs = encoder(imgs)
+            label_imgs = Variable(label_imgs.type(torch.LongTensor)).cuda()
+
+            outputs = network(imgs)
+
+            loss = loss_fn(outputs, label_imgs)
+            loss_value = loss.data.cpu().numpy()
+            batch_losses.append(loss_value)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        epoch_loss = np.mean(batch_losses)
+        epoch_losses_train.append(epoch_loss)
+        with open("%s/epoch_losses_train.pkl" % network.model_dir, "wb") as file:
+            pickle.dump(epoch_losses_train, file)
+        print ("train loss: %g" % epoch_loss)
+        plt.figure(1)
+        plt.plot(epoch_losses_train, "k^")
+        plt.plot(epoch_losses_train, "k")
+        plt.ylabel("loss")
+        plt.xlabel("epoch")
+        plt.title("train loss per epoch")
+        plt.savefig("%s/epoch_losses_train.png" % network.model_dir)
+        plt.close(1)
+
+        print ("####")
+
+        network.eval()
+        batch_losses = []
+        for step, (imgs, label_imgs, img_ids) in enumerate(val_loader):
+            with torch.no_grad():
+                imgs = Variable(imgs).cuda()
+                label_imgs = Variable(label_imgs.type(torch.LongTensor)).cuda()
+
+                outputs = network(imgs)
+                loss = loss_fn(outputs, label_imgs)
+                loss_value = loss.data.cpu().numpy()
+                batch_losses.append(loss_value)
+
+        epoch_loss = np.mean(batch_losses)
+        epoch_losses_val.append(epoch_loss)
+        with open("%s/epoch_losses_val.pkl" % network.model_dir, "wb") as file:
+            pickle.dump(epoch_losses_val, file)
+        print ("val loss: %g" % epoch_loss)
+        plt.figure(1)
+        plt.plot(epoch_losses_val, "k^")
+        plt.plot(epoch_losses_val, "k")
+        plt.ylabel("loss")
+        plt.xlabel("epoch")
+        plt.title("val loss per epoch")
+        plt.savefig("%s/epoch_losses_val.png" % network.model_dir)
+        plt.close(1)
diff --git a/examples/GANwithSelf-taughtLearning/deeplabv3/utils/__init__.py b/examples/GANwithSelf-taughtLearning/deeplabv3/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/GANwithSelf-taughtLearning/deeplabv3/utils/preprocess_data.py b/examples/GANwithSelf-taughtLearning/deeplabv3/utils/preprocess_data.py
new file mode 100644
index 00000000..ca9dc83b
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/deeplabv3/utils/preprocess_data.py
@@ -0,0 +1,190 @@
+# camera-ready
+
+import pickle
+import numpy as np
+import cv2
+import os
+from collections import namedtuple
+
+# (NOTE! this is taken from the official Cityscapes scripts:)
+Label = namedtuple( 'Label' , [
+
+    'name'        , # The identifier of this label, e.g. 'car', 'person', ... .
+                    # We use them to uniquely name a class
+
+    'id'          , # An integer ID that is associated with this label.
+                    # The IDs are used to represent the label in ground truth images
+                    # An ID of -1 means that this label does not have an ID and thus
+                    # is ignored when creating ground truth images (e.g. license plate).
+                    # Do not modify these IDs, since exactly these IDs are expected by the
+                    # evaluation server.
+
+    'trainId'     , # Feel free to modify these IDs as suitable for your method. Then create
+                    # ground truth images with train IDs, using the tools provided in the
+                    # 'preparation' folder. However, make sure to validate or submit results
+                    # to our evaluation server using the regular IDs above!
+                    # For trainIds, multiple labels might have the same ID. Then, these labels
+                    # are mapped to the same class in the ground truth images. For the inverse
+                    # mapping, we use the label that is defined first in the list below.
+                    # For example, mapping all void-type classes to the same ID in training,
+                    # might make sense for some approaches.
+                    # Max value is 255!
+
+    'category'    , # The name of the category that this label belongs to
+
+    'categoryId'  , # The ID of this category. Used to create ground truth images
+                    # on category level.
+
+    'hasInstances', # Whether this label distinguishes between single instances or not
+
+    'ignoreInEval', # Whether pixels having this class as ground truth label are ignored
+                    # during evaluations or not
+
+    'color'       , # The color of this label
+    ] )
+
+# (NOTE! this is taken from the official Cityscapes scripts:)
+labels = [
+    #       name                     id    trainId   category            catId     hasInstances   ignoreInEval   color
+    Label(  'unlabeled'            ,  0 ,      19 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
+    Label(  'ego vehicle'          ,  1 ,      19 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
+    Label(  'rectification border' ,  2 ,      19 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
+    Label(  'out of roi'           ,  3 ,      19 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
+    Label(  'static'               ,  4 ,      19 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
+    Label(  'dynamic'              ,  5 ,      19 , 'void'            , 0       , False        , True         , (111, 74,  0) ),
+    Label(  'ground'               ,  6 ,      19 , 'void'            , 0       , False        , True         , ( 81,  0, 81) ),
+    Label(  'road'                 ,  7 ,        0 , 'flat'            , 1       , False        , False        , (128, 64,128) ),
+    Label(  'sidewalk'             ,  8 ,        1 , 'flat'            , 1       , False        , False        , (244, 35,232) ),
+    Label(  'parking'              ,  9 ,      19 , 'flat'            , 1       , False        , True         , (250,170,160) ),
+    Label(  'rail track'           , 10 ,      19 , 'flat'            , 1       , False        , True         , (230,150,140) ),
+    Label(  'building'             , 11 ,        2 , 'construction'    , 2       , False        , False        , ( 70, 70, 70) ),
+    Label(  'wall'                 , 12 ,        3 , 'construction'    , 2       , False        , False        , (102,102,156) ),
+    Label(  'fence'                , 13 ,        4 , 'construction'    , 2       , False        , False        , (190,153,153) ),
+    Label(  'guard rail'           , 14 ,      19 , 'construction'    , 2       , False        , True         , (180,165,180) ),
+    Label(  'bridge'               , 15 ,      19 , 'construction'    , 2       , False        , True         , (150,100,100) ),
+    Label(  'tunnel'               , 16 ,      19 , 'construction'    , 2       , False        , True         , (150,120, 90) ),
+    Label(  'pole'                 , 17 ,        5 , 'object'          , 3       , False        , False        , (153,153,153) ),
+    Label(  'polegroup'            , 18 ,      19 , 'object'          , 3       , False        , True         , (153,153,153) ),
+    Label(  'traffic light'        , 19 ,        6 , 'object'          , 3       , False        , False        , (250,170, 30) ),
+    Label(  'traffic sign'         , 20 ,        7 , 'object'          , 3       , False        , False        , (220,220,  0) ),
+    Label(  'vegetation'           , 21 ,        8 , 'nature'          , 4       , False        , False        , (107,142, 35) ),
+    Label(  'terrain'              , 22 ,        9 , 'nature'          , 4       , False        , False        , (152,251,152) ),
+    Label(  'sky'                  , 23 ,       10 , 'sky'             , 5       , False        , False        , ( 70,130,180) ),
+    Label(  'person'               , 24 ,       11 , 'human'           , 6       , True         , False        , (220, 20, 60) ),
+    Label(  'rider'                , 25 ,       12 , 'human'           , 6       , True         , False        , (255,  0,  0) ),
+    Label(  'car'                  , 26 ,       13 , 'vehicle'         , 7       , True         , False        , (  0,  0,142) ),
+    Label(  'truck'                , 27 ,       14 , 'vehicle'         , 7       , True         , False        , (  0,  0, 70) ),
+    Label(  'bus'                  , 28 ,       15 , 'vehicle'         , 7       , True         , False        , (  0, 60,100) ),
+    Label(  'caravan'              , 29 ,      19 , 'vehicle'         , 7       , True         , True         , (  0,  0, 90) ),
+    Label(  'trailer'              , 30 ,      19 , 'vehicle'         , 7       , True         , True         , (  0,  0,110) ),
+    Label(  'train'                , 31 ,       16 , 'vehicle'         , 7       , True         , False        , (  0, 80,100) ),
+    Label(  'motorcycle'           , 32 ,       17 , 'vehicle'         , 7       , True         , False        , (  0,  0,230) ),
+    Label(  'bicycle'              , 33 ,       18 , 'vehicle'         , 7       , True         , False        , (119, 11, 32) ),
+    Label(  'license plate'        , -1 ,       19 , 'vehicle'         , 7       , False        , True         , (  0,  0,142) ),
+]
+
+# create a function which maps id to trainId:
+id_to_trainId = {label.id: label.trainId for label in labels}
+id_to_trainId_map_func = np.vectorize(id_to_trainId.get)
+
+train_dirs = ["jena/", "zurich/", "weimar/", "ulm/", "tubingen/", "stuttgart/",
+              "strasbourg/", "monchengladbach/", "krefeld/", "hanover/",
+              "hamburg/", "erfurt/", "dusseldorf/", "darmstadt/", "cologne/",
+              "bremen/", "bochum/", "aachen/"]
+val_dirs = ["frankfurt/", "munster/", "lindau/"]
+test_dirs = ["berlin", "bielefeld", "bonn", "leverkusen", "mainz", "munich"]
+
+cityscapes_data_path = "/home/nailtu/data/cityscapes"
+cityscapes_meta_path = "/home/nailtu/data/cityscapes/meta"
+
+if not os.path.exists(cityscapes_meta_path):
+    os.makedirs(cityscapes_meta_path)
+if not os.path.exists(cityscapes_meta_path + "/label_imgs"):
+    os.makedirs(cityscapes_meta_path + "/label_imgs")
+
+################################################################################
+# convert all labels to label imgs with trainId pixel values (and save to disk):
+################################################################################
+train_label_img_paths = []
+
+img_dir = cityscapes_data_path + "/leftImg8bit/train/"
+label_dir = cityscapes_data_path + "/gtFine/train/"
+for train_dir in train_dirs:
+    print (train_dir)
+
+    train_img_dir_path = img_dir + train_dir
+    train_label_dir_path = label_dir + train_dir
+
+    file_names = os.listdir(train_img_dir_path)
+    for file_name in file_names:
+        img_id = file_name.split("_leftImg8bit.png")[0]
+
+        gtFine_img_path = train_label_dir_path + img_id + "_gtFine_labelIds.png"
+        gtFine_img = cv2.imread(gtFine_img_path, -1) # (shape: (1024, 2048))
+
+        # convert gtFine_img from id to trainId pixel values:
+        label_img = id_to_trainId_map_func(gtFine_img) # (shape: (1024, 2048))
+        label_img = label_img.astype(np.uint8)
+
+        cv2.imwrite(cityscapes_meta_path + "/label_imgs/" + img_id + ".png", label_img)
+        train_label_img_paths.append(cityscapes_meta_path + "/label_imgs/" + img_id + ".png")
+
+img_dir = cityscapes_data_path + "/leftImg8bit/val/"
+label_dir = cityscapes_data_path + "/gtFine/val/"
+for val_dir in val_dirs:
+    print (val_dir)
+
+    val_img_dir_path = img_dir + val_dir
+    val_label_dir_path = label_dir + val_dir
+
+    file_names = os.listdir(val_img_dir_path)
+    for file_name in file_names:
+        img_id = file_name.split("_leftImg8bit.png")[0]
+
+        gtFine_img_path = val_label_dir_path + img_id + "_gtFine_labelIds.png"
+        gtFine_img = cv2.imread(gtFine_img_path, -1) # (shape: (1024, 2048))
+
+        # convert gtFine_img from id to trainId pixel values:
+        label_img = id_to_trainId_map_func(gtFine_img) # (shape: (1024, 2048))
+        label_img = label_img.astype(np.uint8)
+
+        cv2.imwrite(cityscapes_meta_path + "/label_imgs/" + img_id + ".png", label_img)
+
+################################################################################
+# compute the class weigths:
+################################################################################
+print ("computing class weights")
+
+num_classes = 20
+
+trainId_to_count = {}
+for trainId in range(num_classes):
+    trainId_to_count[trainId] = 0
+
+# get the total number of pixels in all train label_imgs that are of each object class:
+for step, label_img_path in enumerate(train_label_img_paths):
+    if step % 100 == 0:
+        print (step)
+
+    label_img = cv2.imread(label_img_path, -1)
+
+    for trainId in range(num_classes):
+        # count how many pixels in label_img which are of object class trainId:
+        trainId_mask = np.equal(label_img, trainId)
+        trainId_count = np.sum(trainId_mask)
+
+        # add to the total count:
+        trainId_to_count[trainId] += trainId_count
+
+# compute the class weights according to the ENet paper:
+class_weights = []
+total_count = sum(trainId_to_count.values())
+for trainId, count in trainId_to_count.items():
+    trainId_prob = float(count)/float(total_count)
+    trainId_weight = 1/np.log(1.02 + trainId_prob)
+    class_weights.append(trainId_weight)
+
+print (class_weights)
+
+with open(cityscapes_meta_path + "/class_weights.pkl", "wb") as file:
+    pickle.dump(class_weights, file, protocol=2) # (protocol=2 is needed to be able to open this file with python2)
diff --git a/examples/GANwithSelf-taughtLearning/deeplabv3/utils/random_code.py b/examples/GANwithSelf-taughtLearning/deeplabv3/utils/random_code.py
new file mode 100644
index 00000000..4c19463f
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/deeplabv3/utils/random_code.py
@@ -0,0 +1,23 @@
+# camera-ready
+
+# this file contains code snippets which I have found (more or less) useful at
+# some point during the project. Probably nothing interesting to see here.
+
+import pickle
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+model_id = "13_2_2_2"
+
+with open("/home/fregu856/exjobb/training_logs/multitask/model_" + model_id + "/epoch_losses_train.pkl", "rb") as file:
+    train_loss = pickle.load(file)
+
+with open("/home/fregu856/exjobb/training_logs/multitask/model_" + model_id + "/epoch_losses_val.pkl", "rb") as file:
+    val_loss = pickle.load(file)
+
+print ("train loss min:", np.argmin(np.array(train_loss)), np.min(np.array(train_loss)))
+
+print ("val loss min:", np.argmin(np.array(val_loss)), np.min(np.array(val_loss)))
diff --git a/examples/GANwithSelf-taughtLearning/deeplabv3/utils/utils.py b/examples/GANwithSelf-taughtLearning/deeplabv3/utils/utils.py
new file mode 100644
index 00000000..b1844c10
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/deeplabv3/utils/utils.py
@@ -0,0 +1,56 @@
+# camera-ready
+
+import torch
+import torch.nn as nn
+
+import numpy as np
+
+def add_weight_decay(net, l2_value, skip_list=()):
+    # https://raberrytv.wordpress.com/2017/10/29/pytorch-weight-decay-made-easy/
+
+    decay, no_decay = [], []
+    for name, param in net.named_parameters():
+        if not param.requires_grad:
+            continue # frozen weights
+        if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
+            no_decay.append(param)
+        else:
+            decay.append(param)
+
+    return [{'params': no_decay, 'weight_decay': 0.0}, {'params': decay, 'weight_decay': l2_value}]
+
+# function for colorizing a label image:
+def label_img_to_color(img):
+    label_to_color = {
+        0: [128, 64,128],
+        1: [244, 35,232],
+        2: [ 70, 70, 70],
+        3: [102,102,156],
+        4: [190,153,153],
+        5: [153,153,153],
+        6: [250,170, 30],
+        7: [220,220,  0],
+        8: [107,142, 35],
+        9: [152,251,152],
+        10: [ 70,130,180],
+        11: [220, 20, 60],
+        12: [255,  0,  0],
+        13: [  0,  0,142],
+        14: [  0,  0, 70],
+        15: [  0, 60,100],
+        16: [  0, 80,100],
+        17: [  0,  0,230],
+        18: [119, 11, 32],
+        19: [81,  0, 81]
+        }
+
+    img_height, img_width = img.shape
+
+    img_color = np.zeros((img_height, img_width, 3))
+    for row in range(img_height):
+        for col in range(img_width):
+            label = img[row, col]
+
+            img_color[row, col] = np.array(label_to_color[label])
+
+    return img_color
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diff --git a/examples/GANwithSelf-taughtLearning/self-taught-learning/__init__.py b/examples/GANwithSelf-taughtLearning/self-taught-learning/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/GANwithSelf-taughtLearning/self-taught-learning/models.py b/examples/GANwithSelf-taughtLearning/self-taught-learning/models.py
new file mode 100644
index 00000000..b1eb6fea
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/self-taught-learning/models.py
@@ -0,0 +1,47 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Encoder(nn.Module):
+    def __init__(self) -> None:
+        super(Encoder, self).__init__()
+        self.enc1 = nn.Conv2d(
+            in_channels=3, out_channels=8, kernel_size=3, stride=2, padding=1
+        )
+        self.enc2 = nn.Conv2d(
+            in_channels=8, out_channels=3, kernel_size=3, stride=1, padding=1
+        )
+
+    def forward(self, x):
+        x = F.relu(self.enc1(x))
+        x = F.relu(self.enc2(x))
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(self) -> None:
+        super(Decoder, self).__init__()
+        self.dec1 = nn.ConvTranspose2d(
+            in_channels=3, out_channels=8, kernel_size=3, stride=1, padding=1, output_padding=0
+        )
+        self.dec2 = nn.ConvTranspose2d(
+            in_channels=8, out_channels=3, kernel_size=3, stride=2, padding=1, output_padding=1
+        )
+
+    def forward(self, x):
+        x = F.relu(self.dec1(x))
+        x = F.relu(self.dec2(x))
+        return x
+
+
+class Autoencoder(nn.Module):
+    def __init__(self) -> None:
+        super(Autoencoder, self).__init__()
+        self.encoder = Encoder()
+        self.decoder = Decoder()
+
+    def forward(self, x):
+        x = self.encoder(x)
+        # print(x.shape)
+        x = self.decoder(x)
+        return x
diff --git a/examples/GANwithSelf-taughtLearning/self-taught-learning/train.py b/examples/GANwithSelf-taughtLearning/self-taught-learning/train.py
new file mode 100644
index 00000000..4ea32483
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/self-taught-learning/train.py
@@ -0,0 +1,104 @@
+from torchvision.utils import save_image
+from models import Autoencoder
+import torch
+import cv2
+import numpy as np
+from torch.utils.data import DataLoader
+import os
+import torch.nn as nn
+import torch.optim as optim
+import csv
+import time
+from util import load_yaml
+
+
+class DatasetAutoEncoder(torch.utils.data.Dataset):
+    def __init__(self, fake_images_path):
+        self.img_dir = fake_images_path
+
+        self.new_img_w = 2048
+
+        self.new_img_h = 1024
+
+        self.examples = []
+
+        file_names = os.listdir(fake_images_path)
+
+        for file_name in file_names:
+            img_path = fake_images_path + file_name
+            self.examples.append({'img_path': img_path})
+
+        self.examples = self.examples[0:60]
+
+        self.num_examples = len(self.examples)
+
+    def __getitem__(self, index):
+        example = self.examples[index]
+
+        img_path = example["img_path"]
+        img = cv2.imread(img_path, -1)
+        img = cv2.resize(img, (self.new_img_w, self.new_img_h),
+                         interpolation=cv2.INTER_NEAREST)
+        img = img / 255.0
+        img = img - np.array([0.485, 0.456, 0.406])
+        img = img / np.array([0.229, 0.224, 0.225])
+        img = np.transpose(img, (2, 0, 1))
+
+        img = img.astype(np.float32)
+
+        img = torch.from_numpy(img)
+        img = torch.Tensor(img)
+        return img
+
+    def __len__(self):
+        return self.num_examples
+
+
+def save_decoded_image(img, name):
+    img = img.view(1, 3, 1024, 2048)
+    save_image(img, name)
+
+
+if __name__ == '__main__':
+    configs = load_yaml('../config.yaml')
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    LEARNING_RATE = configs['STL'][1]['lr']
+    NUM_EPOCHS = configs['STL'][0]['iter']
+    batch_size = configs['STL'][2]['batch_size']
+    name = configs['STL'][3]['name']
+    save_dir = 'train_results/' + name
+    if not os.path.exists(save_dir):
+        os.mkdir(save_dir)
+    net = Autoencoder().to(device)
+    encoder_dataset = DatasetAutoEncoder(fake_images_path='../data/fake_imgs/')
+    encoder_loader = DataLoader(dataset=encoder_dataset, batch_size=batch_size, drop_last=True)
+    criterion = nn.MSELoss()
+    optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE)
+    train_loss = []
+    with open('train_loss1.csv', 'w') as csvfile:
+        writer = csv.writer(csvfile)
+        writer.writerow(['epoch', 'loss'])
+    print('========start training============')
+    start_time = time.time()
+    for epoch in range(1, NUM_EPOCHS + 1):
+        print('======={}========'.format(epoch))
+        running_loss = 0.0
+        for batch_idx, img in enumerate(encoder_loader):
+            img = img.to(device)
+            optimizer.zero_grad()
+            outputs = net(img)
+            loss = criterion(outputs, img)
+            loss.backward()
+            optimizer.step()
+            running_loss += loss.item()
+        loss = running_loss / len(encoder_loader)
+        train_loss.append(loss)
+        print('Epoch {} of {}, Train Loss: {}'.format(
+            epoch, NUM_EPOCHS, loss))
+        with open('train_loss1.csv', 'a') as csvfile:
+            writer = csv.writer(csvfile)
+            writer.writerow([epoch, loss])
+        torch.save(net.encoder.state_dict(), save_dir + '/encoder{}.pth'.format(epoch))
+        save_decoded_image(img[0].cpu().data, name=save_dir + '/original{}.png'.format(epoch))
+        save_decoded_image(outputs[0].cpu().data, name=save_dir + '/decoded{}.png'.format(epoch))
+    print('consume time: {}'.format(time.time() - start_time))
diff --git a/examples/GANwithSelf-taughtLearning/self-taught-learning/train_loss1.csv b/examples/GANwithSelf-taughtLearning/self-taught-learning/train_loss1.csv
new file mode 100644
index 00000000..c99f3e62
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/self-taught-learning/train_loss1.csv
@@ -0,0 +1,5 @@
+epoch,loss
+1,1.1943154335021973
+2,1.1841652989387512
+3,1.1787938475608826
+4,1.1748800873756409
diff --git a/examples/GANwithSelf-taughtLearning/util.py b/examples/GANwithSelf-taughtLearning/util.py
new file mode 100644
index 00000000..005a9cab
--- /dev/null
+++ b/examples/GANwithSelf-taughtLearning/util.py
@@ -0,0 +1,12 @@
+import yaml
+
+
+def load_yaml(path):
+    with open(path) as f:
+        data = yaml.load(f, Loader=yaml.FullLoader)
+        # print(data)
+        return data
+
+
+if __name__ == '__main__':
+    load_yaml('config.yaml')