About exchanging expectation and supremum for conditional Wasserstein GANs
Not on the map. Why?
Abstract (arXiv)
In cases where a Wasserstein GAN depends on a condition the latter is usually handled via an expectation within the loss function. Depending on the way this is motivated, the discriminator is either required to be Lipschitz-1 in both or in only one of its arguments. For the weaker requirement to become usable one needs to exchange a supremum and an expectation. This is a mathematically perilous operation, which is, so far, only partially justified in the literature. This short mathematical note intends to fill this gap and provides the mathematical rationale for discriminators that are only partially Lipschitz-1 for cases where this approach is more appropriate or successful.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 25 Mar 2021.
Builds on
7 of 12 references.
References · 7
- 2017Improved Training of Wasserstein GANscited by 2,223
- 2014Conditional Generative Adversarial Netscited by 2,152
- 2017Wasserstein GANcited by 635
- 2018Deep Bayesian Inversioncited by 60
- 2018Banach Wasserstein GANcited by 56
- 2021High Perceptual Quality Image Denoising with a Posterior Sampling CGANcited by 33
- 2019Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networkscited by 3
Built on by
Citing papers per year: 2023: 1; 2024: 1.
2 papers.