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Statistics > Machine Learning

arXiv:1406.2661 (stat)
[Submitted on 10 Jun 2014]

Title:Generative Adversarial Networks

Authors:Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu,
David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
Download a PDF of the paper titled Generative Adversarial Networks,
by Ian J. Goodfellow and 7 other authors
Download PDF

    Abstract: 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
    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.

Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as:  arXiv:1406.2661 [stat.ML]
          (or arXiv:1406.2661v1 [stat.ML] for this version)
          https://doi.org/10.48550/arXiv.1406.2661
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Ian Goodfellow [view email]
[v1] Tue, 10 Jun 2014 18:58:17 UTC (1,257 KB)
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