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The Wasserstein Generative Adversarial Network (WGAN) is a variant of generative adversarial network (GAN) proposed in 2017 that aims to "improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches".^[1]^[2]

Compared with the original GAN discriminator, the Wasserstein GAN discriminator provides a better learning signal to the generator. This allows the training to be more stable when generator is learning distributions in very high dimensional spaces.

^ Arjovsky, Martin; Chintala, Soumith; Bottou, Léon (2017-07-17). "Wasserstein Generative Adversarial Networks". International Conference on Machine Learning. PMLR: 214–223.
^ Weng, Lilian (2019-04-18). "From GAN to WGAN". arXiv:1904.08994 [cs.LG].