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Generative adversarial networks

Authors:

Ian Goodfellow,

Jean Pouget-Abadie,

David Warde-Farley,

Aaron Courville,

Yoshua BengioAuthors Info & Claims

Communications of the ACM, Volume 63, Issue 11

Pages 139 - 144

https://doi.org/10.1145/3422622

Published: 22 October 2020 Publication History

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Abstract

Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization.

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Index Terms

Generative adversarial networks
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
      2. Computer vision representations
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image Communications of the ACM

Communications of the ACM Volume 63, Issue 11

November 2020

142 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3431460

Editor:
Andrew A. Chien
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 22 October 2020

Published in CACM Volume 63, Issue 11

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