How Well Generative Adversarial Networks Learn Distributions

@article{Liang2021HowWG,
  title={How Well Generative Adversarial Networks Learn Distributions},
  author={Tengyuan Liang},
  journal={J. Mach. Learn. Res.},
  year={2021},
  volume={22},
  pages={228:1-228:41}
}
  • Tengyuan Liang
  • Published 7 November 2018
  • Computer Science
  • J. Mach. Learn. Res.
This paper studies the rates of convergence for learning distributions implicitly with the adversarial framework and Generative Adversarial Networks (GAN), which subsume Wasserstein, Sobolev, MMD GAN, and Generalized/Simulated Method of Moments (GMM/SMM) as special cases. We study a wide range of parametric and nonparametric target distributions, under a host of objective evaluation metrics. We investigate how to obtain a good statistical guarantee for GANs through the lens of regularization… 

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