English

From GAN to WGAN

Machine Learning 2019-04-22 v1 Machine Learning

Abstract

This paper explains the math behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs' training by adopting a smooth metric for measuring the distance between two probability distributions.

Keywords

Cite

@article{arxiv.1904.08994,
  title  = {From GAN to WGAN},
  author = {Lilian Weng},
  journal= {arXiv preprint arXiv:1904.08994},
  year   = {2019}
}

Comments

12 pages, 9 figures

R2 v1 2026-06-23T08:44:20.464Z