English

NIPS 2016 Tutorial: Generative Adversarial Networks

Machine Learning 2017-04-05 v4

Abstract

This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs, and (5) state-of-the-art image models that combine GANs with other methods. Finally, the tutorial contains three exercises for readers to complete, and the solutions to these exercises.

Keywords

Cite

@article{arxiv.1701.00160,
  title  = {NIPS 2016 Tutorial: Generative Adversarial Networks},
  author = {Ian Goodfellow},
  journal= {arXiv preprint arXiv:1701.00160},
  year   = {2017}
}

Comments

v2-v4 are all typo fixes. No substantive changes relative to v1

R2 v1 2026-06-22T17:38:30.943Z