  In this paper, we introduce PeerGAN, 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 that
are able to fool both discriminators. Different from existing methods, we
introduce another game between $D_1$ and $D_2$ to discourage their agreement
and therefore increase the level of diversity of the generated samples. This
property helps avoid 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
PeerGAN as well as stability of the min-max game. It's worth mentioning that
PeerGAN operates in the unsupervised setting, and the additional game 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 PeerGAN outperforms competitive
baseline work in generating diverse and high-quality samples, while only
introduces negligible computation cost.
