Generative adversarial networks

2020/10/22 by Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza +5 · 13,889 citations
Computer Science · #Adversarial system #Artificial intelligence #Computational Physics and Python Applications #Computer science #Deep learning #Explainable Artificial Intelligence (XAI) #Generative Adversarial Networks and Image Synthesis #Generative Design #Generative adversarial network #Generative grammar #Generative model #Machine learning #Variety (cybernetics)

paper · pdf · doi:10.1145/3422622

published in Communications of the ACM 63(11), 139-144 (Association for Computing Machinery)

openalex publication_date 2020/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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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