• DOI:10.1007/978-1-4842-3679-6_8
Corpus ID: 12209503

Generative Adversarial Networks

@article{Goodfellow2014GenerativeAN,
  title={Generative Adversarial Networks},
  author={Ian J. Goodfellow and Jean Pouget-Abadie and M. Mirza and B. Xu and David Warde-Farley and Sherjil Ozair and Aaron C. Courville and Yoshua Bengio},
  journal={ArXiv},
  year={2014},
  volume={abs/1406.2661}
}

Ian J. Goodfellow, Jean Pouget-Abadie, +5 authors Yoshua Bengio

Published 2014

Computer Science, Mathematics

ArXiv

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: 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… CONTINUE READING

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