WGAN
Wasserstein GAN
**Wasserstein GAN**, or **WGAN**, is a type of generative adversarial network that minimizes an approximation of the Earth-Mover's distance (EM) rather than the Jensen-Shannon divergence as in the original [GAN](https://paperswithcode.com/method/gan) formulation. It leads to more stable training than original GANs with less evidence of mode collapse, as well as meaningful curves that can be used for debugging and searching hyperparameters.
Wasserstein GAN
https://paperswithcode.com/paper/wasserstein-gan
Wasserstein GAN