  We propose in this paper a novel approach to tackle the problem of mode
collapse encountered in generative adversarial network (GAN). Our idea is
intuitive but proven to be very effective, especially in addressing some key
limitations of GAN. In essence, it combines the Kullback-Leibler (KL) and
reverse KL divergences into a unified objective function, thus it exploits the
complementary statistical properties from these divergences to effectively
diversify the estimated density in capturing multi-modes. We term our method
dual discriminator generative adversarial nets (D2GAN) which, unlike GAN, has
two discriminators; and together with a generator, it also has the analogy of a
minimax game, wherein a discriminator rewards high scores for samples from data
distribution whilst another discriminator, conversely, favoring data from the
generator, and the generator produces data to fool both two discriminators. We
develop theoretical analysis to show that, given the maximal discriminators,
optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL
divergences between data distribution and the distribution induced from the
data generated by the generator, hence effectively avoiding the mode collapsing
problem. We conduct extensive experiments on synthetic and real-world
large-scale datasets (MNIST, CIFAR-10, STL-10, ImageNet), where we have made
our best effort to compare our D2GAN with the latest state-of-the-art GAN's
variants in comprehensive qualitative and quantitative evaluations. The
experimental results demonstrate the competitive and superior performance of
our approach in generating good quality and diverse samples over baselines, and
the capability of our method to scale up to ImageNet database.
