HSGAN: Reducing mode collapse in GANs by the latent code distance of homogeneous samples

Abstract

In this paper, we propose HSGAN, a novel generative adversarial network (GAN) variant that plays an adversarial game on the distance between two homogeneous samples (HS) in the latent space. HSGAN alleviates the notorious problem of mode collapse by maintaining a certain distance between the latent code of the generated data. Moreover, HSGAN is directly trained on the encoder and the generator, thereby gaining the ability to conduct inference without introducing any other model complexity. We prove theoretically that the objective function is designed to minimize the f-divergence between the distributions of the generated data and the real data. Extensive experiments on a series of synthetic and real image benchmark datasets demonstrate that HSGAN generates diverse images while keeping high quality, and it generally outperforms other GANs that target at the mode collapse problem.