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.

Keywords

Generative grammarComputer scienceAdversarial systemArtificial intelligenceGenerative DesignMachine learningGenerative modelVariety (cybernetics)Generative adversarial networkDeep learning

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Publication Info

Year
2020
Type
article
Volume
63
Issue
11
Pages
139-144
Citations
11829
Access
Closed

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Cite This

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza et al. (2020). Generative adversarial networks. Communications of the ACM , 63 (11) , 139-144. https://doi.org/10.1145/3422622

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DOI
10.1145/3422622

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