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We propose to incorporate adversarial dropout in generative multi-adversarialnetworks, by omitting or dropping out, the feedback of each discriminator inthe framework with some probability at the end of each batch. Our approachforces the single generator not to constrain its output to satisfy a singlediscriminator, but, instead, to satisfy a dynamic ensemble of discriminators.We show that this leads to a more generalized generator, promoting variety inthe generated samples and avoiding the common mode collapse problem commonlyexperienced with generative adversarial networks (GANs). We further provideevidence that the proposed framework, named Dropout-GAN, promotes samplediversity both within and across epochs, eliminating mode collapse andstabilizing training.

Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators