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Talk

Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)

  • Lisa Maria Kreusser (University of Bath)
Live Stream
Attention: this event is cancelled

Abstract

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.

Attention: this event is cancelled

Attention: this event is cancelled
seminar
25.04.24 16.05.24

Math Machine Learning seminar MPI MIS + UCLA

MPI for Mathematics in the Sciences Live Stream

Katharina Matschke

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