Unsupervised Representation Learning with Prior-Free and Adversarial Mechanism Embedded Autoencoders

Most state-of-the-art methods for representation learning are supervised, which require a large number of labeled data. This paper explores a novel unsupervised approach for learning visual representation. We introduce an image-wise discrimination criterion in addition to a pixel-wise reconstruction criterion to model both individual images and the difference between original images and reconstructed ones during neural network training. These criteria induce networks to focus on not only local features but also global high-level representations, so as to provide a competitive alternative to supervised representation learning methods, especially in the case of limited labeled data. We further introduce a competition mechanism to drive each component to increase its capability to win its adversary. In this way, the identity of representations and the likeness of reconstructed images to original ones are alternately improved. Experimental results on several tasks demonstrate the effectiveness of our approach.