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

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