The original paper on GANs

Released in: Generative Adversarial Networks



The authors propose a new framework for estimating generative models via an adversarial process, in which two models are trained simultaneously: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.


Year Released

Key Links & Stats



Generative Adversarial Networks

@inproceedings{NIPS2014_5ca3e9b1, author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua}, booktitle = {Advances in Neural Information Processing Systems}, editor = {Z. Ghahramani and M. Welling and C. Cortes and N. Lawrence and K. Q. Weinberger}, pages = {}, publisher = {Curran Associates, Inc.}, title = {Generative Adversarial Nets}, url = {}, volume = {27}, year = {2014} }

ML Tasks

  1. General
  2. Image Generation

ML Platform

  1. Pytorch


  1. General
  2. Still Image


  1. General

CG Platform

  1. Not Applicable

Related organizations

Universite de Montreal