  We propose a unified game-theoretical framework to perform classification and
conditional image generation given limited supervision. It is formulated as a
three-player minimax game consisting of a generator, a classifier and a
discriminator, and therefore is referred to as Triple Generative Adversarial
Network (Triple-GAN). The generator and the classifier characterize the
conditional distributions between images and labels to perform conditional
generation and classification, respectively. The discriminator solely focuses
on identifying fake image-label pairs. Under a nonparametric assumption, we
prove the unique equilibrium of the game is that the distributions
characterized by the generator and the classifier converge to the data
distribution. As a byproduct of the three-player mechanism, Triple-GAN is
flexible to incorporate different semi-supervised classifiers and GAN
architectures. We evaluate Triple-GAN in two challenging settings, namely,
semi-supervised learning and the extreme low data regime. In both settings,
Triple-GAN can achieve excellent classification results and generate meaningful
samples in a specific class simultaneously. In particular, using a commonly
adopted 13-layer CNN classifier, Triple-GAN outperforms extensive
semi-supervised learning methods substantially on more than 10 benchmarks no
matter data augmentation is applied or not.
