diff --git a/test/test_transforms.py b/test/test_transforms.py
index acee9629f65..da6b0c34645 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -258,6 +258,91 @@ def test_lambda(self):
         # Checking if Lambda can be printed as string
         trans.__repr__()
 
+    def test_random_apply(self):
+        random_state = random.getstate()
+        random.seed(42)
+        random_apply_transform = transforms.RandomApply(
+            [
+                transforms.RandomRotation((-45, 45)),
+                transforms.RandomHorizontalFlip(),
+                transforms.RandomVerticalFlip(),
+            ], p=0.75
+        )
+        img = transforms.ToPILImage()(torch.rand(3, 10, 10))
+        num_samples = 250
+        num_applies = 0
+        for _ in range(num_samples):
+            out = random_apply_transform(img)
+            if out != img:
+                num_applies += 1
+
+        p_value = stats.binom_test(num_applies, num_samples, p=0.75)
+        random.setstate(random_state)
+        assert p_value > 0.0001
+
+        # Checking if RandomApply can be printed as string
+        random_apply_transform.__repr__()
+
+    def test_random_choice(self):
+        random_state = random.getstate()
+        random.seed(42)
+        random_choice_transform = transforms.RandomChoice(
+            [
+                transforms.Resize(15),
+                transforms.Resize(20),
+                transforms.CenterCrop(10)
+            ]
+        )
+        img = transforms.ToPILImage()(torch.rand(3, 25, 25))
+        num_samples = 250
+        num_resize_15 = 0
+        num_resize_20 = 0
+        num_crop_10 = 0
+        for _ in range(num_samples):
+            out = random_choice_transform(img)
+            if out.size == (15, 15):
+                num_resize_15 += 1
+            elif out.size == (20, 20):
+                num_resize_20 += 1
+            elif out.size == (10, 10):
+                num_crop_10 += 1
+
+        p_value = stats.binom_test(num_resize_15, num_samples, p=0.33333)
+        assert p_value > 0.0001
+        p_value = stats.binom_test(num_resize_20, num_samples, p=0.33333)
+        assert p_value > 0.0001
+        p_value = stats.binom_test(num_crop_10, num_samples, p=0.33333)
+        assert p_value > 0.0001
+
+        random.setstate(random_state)
+        # Checking if RandomChoice can be printed as string
+        random_choice_transform.__repr__()
+
+    def test_random_order(self):
+        random_state = random.getstate()
+        random.seed(42)
+        random_order_transform = transforms.RandomOrder(
+            [
+                transforms.Resize(20),
+                transforms.CenterCrop(10)
+            ]
+        )
+        img = transforms.ToPILImage()(torch.rand(3, 25, 25))
+        num_samples = 250
+        num_normal_order = 0
+        resize_crop_out = transforms.CenterCrop(10)(transforms.Resize(20)(img))
+        for _ in range(num_samples):
+            out = random_order_transform(img)
+            if out == resize_crop_out:
+                num_normal_order += 1
+
+        p_value = stats.binom_test(num_normal_order, num_samples, p=0.5)
+        random.setstate(random_state)
+        assert p_value > 0.0001
+
+        # Checking if RandomOrder can be printed as string
+        random_order_transform.__repr__()
+
     def test_to_tensor(self):
         test_channels = [1, 3, 4]
         height, width = 4, 4
diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
index aceb296adf3..caf4261335e 100644
--- a/torchvision/transforms/transforms.py
+++ b/torchvision/transforms/transforms.py
@@ -16,9 +16,9 @@
 from . import functional as F
 
 __all__ = ["Compose", "ToTensor", "ToPILImage", "Normalize", "Resize", "Scale", "CenterCrop", "Pad",
-           "Lambda", "RandomCrop", "RandomHorizontalFlip", "RandomVerticalFlip", "RandomResizedCrop",
-           "RandomSizedCrop", "FiveCrop", "TenCrop", "LinearTransformation", "ColorJitter", "RandomRotation",
-           "Grayscale", "RandomGrayscale"]
+           "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop", "RandomHorizontalFlip",
+           "RandomVerticalFlip", "RandomResizedCrop", "RandomSizedCrop", "FiveCrop", "TenCrop", "LinearTransformation",
+           "ColorJitter", "RandomRotation", "Grayscale", "RandomGrayscale"]
 
 
 class Compose(object):
@@ -261,6 +261,77 @@ def __repr__(self):
         return self.__class__.__name__ + '()'
 
 
+class RandomTransforms(object):
+    """Base class for a list of transformations with randomness
+
+    Args:
+        transforms (list or tuple): list of transformations
+    """
+
+    def __init__(self, transforms):
+        assert isinstance(transforms, (list, tuple))
+        self.transforms = transforms
+
+    def __call__(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + '('
+        for t in self.transforms:
+            format_string += '\n'
+            format_string += '    {0}'.format(t)
+        format_string += '\n)'
+        return format_string
+
+
+class RandomApply(RandomTransforms):
+    """Apply randomly a list of transformations with a given probability
+
+    Args:
+        transforms (list or tuple): list of transformations
+        p (float): probability
+    """
+
+    def __init__(self, transforms, p=0.5):
+        super(RandomApply, self).__init__(transforms)
+        self.p = p
+
+    def __call__(self, img):
+        if self.p < random.random():
+            return img
+        for t in self.transforms:
+            img = t(img)
+        return img
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + '('
+        format_string += '\n    p={}'.format(self.p)
+        for t in self.transforms:
+            format_string += '\n'
+            format_string += '    {0}'.format(t)
+        format_string += '\n)'
+        return format_string
+
+
+class RandomOrder(RandomTransforms):
+    """Apply a list of transformations in a random order
+    """
+    def __call__(self, img):
+        order = list(range(len(self.transforms)))
+        random.shuffle(order)
+        for i in order:
+            img = self.transforms[i](img)
+        return img
+
+
+class RandomChoice(RandomTransforms):
+    """Apply single transformation randomly picked from a list
+    """
+    def __call__(self, img):
+        t = random.choice(self.transforms)
+        return t(img)
+
+
 class RandomCrop(object):
     """Crop the given PIL Image at a random location.