EntGAN: A Distributed GAN Framework Baed on Multi-task

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+# macOS
+*/.DS_Store 

docs/proposals/EntGAN.md

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+# Integrate GAN and Self-taught Learning into Sedna Lifelong Learning to Handle Unknown Tasks
+
+## Motivation
+
+In the process of Sedna 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 Sedna 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/EntGAN%20overview.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 Sedna lifelong learning. The network is shown below [GAN Desin](images/EntGAN%20GAN.png).    
+
+![](images/EntGAN%20GAN.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/EntGAN%20self-taught%20learning.png)