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生成對抗網路 (Generative adversarial networks)

說明

生成對抗網路是非監督式學習的一種方法,通過讓兩個神經網路相互博弈的方式進行學習。該方法由伊恩·古德費洛等人於2014年提出。 生成對抗網絡由一個生成網絡與一個判別網絡組成。生成網絡從潛在空間中隨機取樣作為輸入,其輸出結果需要盡量模仿訓練集中的真實樣本。 維基百科
由 IJ Goodfellow 著作2014被引用 39119 次Abstract: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two ...
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GAN 是2014年蒙特婁大學博士生Ian Goodfellow 提出來的(真的是好傢伙!) ... 在GAN架構下,偽造者(counterfeiter)就稱為『生成模型』(generative model),警察稱 ...
A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in June 2014.
由 IJ Goodfellow 著作被引用 39119 次Generative Adversarial Nets. Ian J. Goodfellow∗, Jean Pouget-Abadie†, Mehdi Mirza, Bing Xu, David Warde-Farley,. Sherjil Ozair‡, Aaron Courville, ...
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2019年6月17日Generative Adversarial Networks, or GANs, are a deep-learning-based generative model. More generally, GANs are a model architecture for training ...
2019年10月8日Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. GANs are generative models: they create new ...
Generative adversarial networks (GANs) are algorithmic architectures that use two neural networks, pitting one against the other (thus the “adversarial”) in ...
由 I Goodfellow 著作2020被引用 1475 次Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem.
2014年12月8日Generative adversarial nets ... We propose a new framework for estimating generative models via an adversarial process, in which we ...

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