Generative Adversarial Network (GAN)

A GAN, or Generative Adversarial Network, is a generative model that simultaneously trains two models: a generative model $G$ that captures the data distribution, and a discriminative model $D$ that estimates the probability that a sample came from the training data rather than $G$.The training procedure for $G$ is to maximize the probability of $D$ making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions $G$ and $D$, a unique solution exists, with $G$ recovering the training data distribution and $D$ equal to $\frac{1}{2}$everywhere. In the case where $G$ and $D$ are defined by multilayer perceptrons, the entire system can be trained with backpropagation.
相关学科: Image GenerationData AugmentationImage-to-Image TranslationCganSuper-ResolutionWGANConvolutionSSIMCycleGANVAE









Yoshua Bengio

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Yi Chen

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Michael I. Jordan

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Trevor Darrell

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Bernhard Schölkopf

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Li Fei-Fei

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Mingshui Chen

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Léon Bottou

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Jian Yang

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Karen Simonyan

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