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The success of deep neural networks often relies on a large amount of labeledexamples, which can be difficult to obtain in many real scenarios. To addressthis challenge, unsupervised methods are strongly preferred for training neuralnetworks without using any labeled data. In this paper, we present a novelparadigm of unsupervised representation learning by Auto-EncodingTransformation (AET) in contrast to the conventional Auto-Encoding Data (AED)approach. Given a randomly sampled transformation, AET seeks to predict itmerely from the encoded features as accurately as possible at the output end.The idea is the following: as long as the unsupervised features successfullyencode the essential information about the visual structures of original andtransformed images, the transformation can be well predicted. We will show thatthis AET paradigm allows us to instantiate a large variety of transformations,from parameterized, to non-parameterized and GAN-induced ones. Our experimentsshow that AET greatly improves over existing unsupervised approaches, settingnew state-of-the-art performances being greatly closer to the upper bounds bytheir fully supervised counterparts on CIFAR-10, ImageNet and Places datasets.

AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data