Improving the fairness of the min-max game in GANs training

Generative adversarial networks (GANs) have achieved great success and become more and more popular in recent years. However, understanding of the min-max game in GANs training is still limited. In this paper, we first utilize information game theory to analyze the min-max game in GANs and introduce a new viewpoint on the GANs training that the min-max game in existing GANs is unfair during training, leading to sub-optimal convergence. To tackle this, we propose a novel GAN called Information Gap GAN (IGGAN), which consists of one generator (G) and two discriminators (D1 and D2). Specifically, we apply different data augmentation methods to D1 and D2, respectively. The information gap between different data augmentation methods can change the information received by each player in the min-max game and lead to all three players G, D1 and D2 in IGGAN obtaining incomplete information, which improves the fairness of the min-max game, yielding better convergence. We conduct extensive experiments for large-scale and limited data settings on several common datasets with two backbones, i.e., BigGAN and StyleGAN2. The results demonstrate that IGGAN can achieve a higher Inception Score (IS) and a lower Frechet Inception Distance (FID) compared with other GANs. Codes are available at https://github.com/zzhang05/IGGAN