diff --git a/torchvision/transforms.py b/torchvision/transforms.py
index 2be81389c2c..eca4d9cada8 100644
--- a/torchvision/transforms.py
+++ b/torchvision/transforms.py
@@ -208,3 +208,102 @@ def __call__(self, img):
         scale = Scale(self.size, interpolation=self.interpolation)
         crop = CenterCrop(self.size)
         return crop(scale(img))
+
+
+
+class Lighting(object):
+    """Lighting noise(AlexNet - style PCA - based noise)"""
+
+    def __init__(self, alphastd, eigval, eigvec):
+        self.alphastd = alphastd
+        self.eigval = eigval
+        self.eigvec = eigvec
+
+    def __call__(self, img):
+        if self.alphastd == 0:
+            return img
+
+        alpha = torch.Tensor(3).normal_(0, self.alphastd)
+        rgb = self.eigvec.clone()\
+            .mul(alpha.view(1, 3).expand(3, 3))\
+            .mul(self.eigval.view(1, 3).expand(3, 3))\
+            .sum(1).squeeze()
+
+        img.add_(rgb.view(3, 1, 1).expand_as(img))
+        return img
+
+
+class Grayscale(object):
+
+    def __call__(self, img):
+        img[0].mul_(0.299).add_(0.587, img[1]).add_(0.114, img[2])
+        img[1].copy_(img[0])
+        img[2].copy_(img[0])
+        return img
+
+
+class Saturation(object):
+
+    def __init__(self, var):
+        self.var = var
+
+    def __call__(self, img):
+        gs = Grayscale()(img.clone())
+        alpha = random.uniform(0, self.var)
+        img.lerp_(gs, alpha)
+
+        return img
+
+
+class Brightness(object):
+
+    def __init__(self, var):
+        self.var = var
+
+    def __call__(self, img):
+        gs = img.new().resize_as_(img).zero_()
+        alpha = random.uniform(0, self.var)
+        img.lerp_(gs, alpha)
+
+        return img
+
+
+class Contrast(object):
+
+    def __init__(self, var):
+        self.var = var
+
+    def __call__(self, img):
+        gs = Grayscale()(img.clone())
+        gs.fill_(gs.mean())
+        alpha = random.uniform(0, self.var)
+        img.lerp_(gs, alpha)
+        return img
+
+
+class RandomOrder(object):
+    """ Composes several transforms together in random order.
+    """
+
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, img):
+        if self.transforms is None:
+            return img
+        order = torch.randperm(len(self.transforms))
+        for i in order:
+            img = self.transforms[i](img)
+        return img
+
+
+class ColorJitter(RandomOrder):
+
+    def __init__(self, brightness=0.4, contrast=0.4, saturation=0.4):
+        self.transforms = []
+        if brightness != 0:
+            self.transforms.append(Brightness(brightness))
+        if contrast != 0:
+            self.transforms.append(Contrast(contrast))
+        if saturation != 0:
+            self.transforms.append(Saturation(saturation))