We propose in this paper a novel approach to address the mode collapse problem in Generative Adversarial Nets (GANs) by training many generators. The training procedure is formulated as a minimax game among many generators, one classifier, and one discriminator. Generators produce data to fool the discriminator while staying within the decision boundary defined by the classifier as much as possible; the classifier estimates the probability that a sample comes from each of the generators; and the discriminator estimates the probability that a sample comes from the training data rather than from generators. We develop theoretical analysis to show that at an equilibrium of this system, the Jensen-Shannon divergence between the equally weighted mixture of all generators' distributions and the empirical data distribution is minimal while the Jensen-Shannon divergence among generators' distributions is maximal. Generators can be trained efficiently by utilizing parameter sharing, thus adding minimal cost to the basic GAN model. We conduct extensive experiments on synthetic and real-world large-scale datasets (CIFAR-10, STL-10 and ImageNet) to evaluate the effectiveness of our proposed method. Experimental results demonstrate the state-of-the-art performance of our approach in generating diverse and visually appealing samples over the latest GAN's variants.
REF introduces MGAN where a mixture of generators are trained, mainly to solve the mode collapsing problem.
Multi-Generator Generative Adversarial Nets
ArXiv
ArXiv
Mathematics Computer Science