  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 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 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, and FFHQ) demonstrate that DuelGAN
outperforms competitive baseline work in generating diverse and high-quality
samples, while only introduces negligible computation cost.
