Reviews: Triple Generative Adversarial Nets

In this paper, the authors propose a new formulation of adversarial networks for image generation, that incorporates three networks instead of the usual generator G and discriminator D. In addition, they include a classifier C, which cooperates with G to learn a compatible joint distribution (X,Y) over images and labels. The authors show how this formulation overcomes pitfalls of previous class-conditional GANs; namely that class-conditional generator and discriminator networks have competing objectives that may prevent them from learning the true distribution and preventing G from accurately generating class-conditional samples. The authors identify the following deficiency in class-conditional GAN setups: "The competition between G and D essentially arises from their two-player formulation, where a single discriminator network has to play two incompatible roles--identifying fake samples and predicting labels". The argument goes that if G were perfect, then a class-conditional D has an equal incentive to output 0 since the sample comes from G, and to output 1 since the image matches the label. This might force D to systematically underperform as a classifier, and therefore prevent G from learning to produce accurate class-conditional samples.