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