We first discuss the theory behind WGANs, most notably Optimal Transport. We then explore the mathematical theory behind GANs, introduce their limitations, and provide an overview of ways to address the training instability in practice. Finally, we talk about how the Wasserstein distance can be used as an alternative distribution measure, giving rise to WGAN.
We provide some experiments to compare the two on manually defined distributions here
See pdf for the full text.