DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training.

In this paper, we introduce DuelGAN, a generative adversarial network (GAN) solution to improve the stability of the generated samples and to mitigate mode collapse. Built upon the Vanilla GAN’s two-player game between the discriminator \(D_1\) and the generator G, we introduce a peer discriminator \(D_2\) to the min-max game. Similar to previous work using two discriminators, the first role of both \(D_1\), \(D_2\) is to distinguish between generated samples and real ones, while the generator tries to generate high-quality samples which are able to fool both discriminators. Different from existing methods, we introduce a duel between \(D_1\) and \(D_2\) to discourage their agreement and therefore increase the level of diversity of the generated samples. This property alleviates the issue of early mode collapse by preventing \(D_1\) and \(D_2\) from converging too fast. We provide theoretical analysis for the equilibrium of the min-max game formed among \(G,D_1,D_2\). We offer convergence behavior of DuelGAN as well as stability of the min-max game. It’s worth mentioning that DuelGAN operates in the unsupervised setting, and the duel between \(D_1\) and \(D_2\) does not need any label supervision. Experiments results on a synthetic dataset and on real-world image datasets (MNIST, Fashion MNIST, CIFAR-10, STL-10, CelebA, VGG) demonstrate that DuelGAN outperforms competitive baseline work in generating diverse and high-quality samples, while only introduces negligible computation cost. Our code is publicly available at https://github.com/UCSC-REAL/DuelGAN.