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

  • Lisa Maria Kreusser (University of Bath)
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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.


3/7/24 3/14/24

Math Machine Learning seminar MPI MIS + UCLA

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