  Generative Adversarial Networks (GANs) are a type of generative model which
have received much attention due to their ability to model complex real-world
data. Despite their recent successes, the process of training GANs remains
challenging, suffering from instability problems such as non-convergence,
vanishing or exploding gradients, and mode collapse. In recent years, a diverse
set of approaches have been proposed which focus on stabilizing the GAN
training procedure. The purpose of this survey is to provide a comprehensive
overview of the GAN training stabilization methods which can be found in the
literature. We discuss the advantages and disadvantages of each approach, offer
a comparative summary, and conclude with a discussion of open problems.
