  In this paper we present a method for learning a discriminative classifier
from unlabeled or partially labeled data. Our approach is based on an objective
function that trades-off mutual information between observed examples and their
predicted categorical class distribution, against robustness of the classifier
to an adversarial generative model. The resulting algorithm can either be
interpreted as a natural generalization of the generative adversarial networks
(GAN) framework or as an extension of the regularized information maximization
(RIM) framework to robust classification against an optimal adversary. We
empirically evaluate our method - which we dub categorical generative
adversarial networks (or CatGAN) - on synthetic data as well as on challenging
image classification tasks, demonstrating the robustness of the learned
classifiers. We further qualitatively assess the fidelity of samples generated
by the adversarial generator that is learned alongside the discriminative
classifier, and identify links between the CatGAN objective and discriminative
clustering algorithms (such as RIM).
