Lisa Maria Kreusser (University of Bath)
Thursday, June 23, 2022 - 17:00
Virtual event (Videobroadcast) - link for registration
Max-Planck-Institut fuer Mathematik in den Naturwissenschaften, 04103 Leipzig
Wasserstein GANs (WGANs) are based on the idea of minimising the Wasserstein distance between a real and a generated distribution. In this talk, we provide an in-depth mathematical analysis of differences between the theoretical setup and the reality of training WGANs. We gather both theoretical and empirical evidence that the WGAN loss is not a meaningful approximation of the Wasserstein distance. Moreover, we argue that the Wasserstein distance is not even a desirable loss function for deep generative models, and conclude that the success of WGANs can be attributed to a failure to approximate the Wasserstein distance.
submitted by Katharina Matschke (Katharina.Matschke@mis.mpg.de, 0341 9959 50)