Generative Adversarial Networks
Generative Adversarial Networks
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DOI：
10.1145/3422622
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发表时间：
2020-11-01
影响因子：
22.7
通讯作者：
Bengio, Yoshua
中科院分区：
文献类型：
--
作者：
Goodfellow, Ian;Pouget-Abadie, Jean;Bengio, Yoshua
创成对抗网络是一种为解决创成建模问题而设计的人工智能算法。生成式模型的目标是研究训练示例的集合，并了解生成这些示例的概率分布。生成对抗网络（GAN）能够从估计的概率分布中生成更多的示例。基于深度学习的生成模型很常见，但GAN是最成功的生成模型之一（特别是在生成逼真的高分辨率图像方面）。GAN已经成功地应用于各种各样的任务（大部分是在研究环境中），但仍然存在独特的挑战和研究机会，因为它们是基于博弈论的，而大多数其他生成式建模方法是基于优化的。
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.