  Generative Adversarial Networks (GANs) have become one of the dominant
methods for deep generative modeling. Despite their demonstrated success on
multiple vision tasks, GANs are difficult to train and much research has been
dedicated towards understanding and improving their gradient-based learning
dynamics. Here, we investigate the use of coevolution, a class of black-box
(gradient-free) co-optimization techniques and a powerful tool in evolutionary
computing, as a supplement to gradient-based GAN training techniques.
Experiments on a simple model that exhibits several of the GAN gradient-based
dynamics (e.g., mode collapse, oscillatory behavior, and vanishing gradients)
show that coevolution is a promising framework for escaping degenerate GAN
training behaviors.
