Max-Boost-GAN: Max Operation to Boost Generative Ability of Generative Adversarial Networks

Generative adversarial networks (GANs) can be used to learn a generation function from a joint probability distribution as an input, and then visual samples with semantic properties can be generated from a marginal probability distribution. In this paper, we propose a novel algorithm named Max-Boost-GAN, which is demonstrated to boost the generative ability of GANs when the error of generation is upper bounded. Moreover, the Max-Boost-GAN can be used to learn the generation functions from two marginal probability distributions as the input, and samples of higher visual quality and variety could be generated from the joint probability distribution. Finally, novel objective functions are proposed for obtaining convergence during training the Max-Boost-GAN. Experiments on the generation of binary digits and RGB human faces show that the Max-Boost-GAN achieves boosted ability of generation as expected.