Communications of the ACM· 2020Q1
Generative adversarial networks
- 13,777citations
- Q1SCImago
- 2020year
Short summary
Generative Adversarial Networks (GANs) are a novel AI algorithm that learns the underlying probability distribution of training data to generate new, realistic examples, particularly excelling at high-resolution image generation.
AI-generated from the title and abstract; the full text is not read.
Key points
- GANs are AI algorithms designed for generative modeling.
- They learn the probability distribution of training data.
- GANs can generate new examples from the learned distribution.
- They are particularly successful at generating realistic, high-resolution images.
- GANs are based on game theory, differentiating them from optimization-based models.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Communications of the ACM, 2020 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science