Reviews: Structured Generative Adversarial Networks

Summary: This paper proposes a novel GAN structure for semi-supervised learning, a setting in which there exist a small dataset with class labels along with a larger unlabeled dataset. The main idea of this paper is to disentangle the labels (y) from the hidden states (z) using two GAN problems that represent p(x,y) and p(x,z). The generator is shared between both GAN problems, but each problem is trained simultaneously using ALI[4]. There are two adversarial games defined for training the joints p(x, y) and p(x, z). Two "collaborative games" are also defined in order to better disentangle y from z and enforce structure on y.