Generative adversarial networks
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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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The record
- Venue
- Communications of the ACM
- Topic
- Generative Adversarial Networks and Image Synthesis
- Field
- Computer Science
- Canadian institutions
- Université de Montréal
- Funders
- —
- Keywords
- Generative grammarComputer scienceAdversarial systemArtificial intelligenceGenerative DesignMachine learningGenerative modelVariety (cybernetics)Generative adversarial networkDeep learning
- Has abstract in OpenAlex
- yes