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Traditional generative adversarial networks (GAN) and many of its variantsare trained by minimizing the KL or JS-divergence loss that measures how closethe generated data distribution is from the true data distribution. A recentadvance called the WGAN based on Wasserstein distance can improve on the KL andJS-divergence based GANs, and alleviate the gradient vanishing, instability,and mode collapse issues that are common in the GAN training. In this work, weaim at improving on the WGAN by first generalizing its discriminator loss to amargin-based one, which leads to a better discriminator, and in turn a bettergenerator, and then carrying out a progressive training paradigm involvingmultiple GANs to contribute to the maximum margin ranking loss so that the GANat later stages will improve upon early stages. We call this method Gang ofGANs (GoGAN). We have shown theoretically that the proposed GoGAN can reducethe gap between the true data distribution and the generated data distributionby at least half in an optimally trained WGAN. We have also proposed a new wayof measuring GAN quality which is based on image completion tasks. We haveevaluated our method on four visual datasets: CelebA, LSUN Bedroom, CIFAR-10,and 50K-SSFF, and have seen both visual and quantitative improvement overbaseline WGAN.

Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking