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What is: Generative Adversarial Network?

SourceGenerative Adversarial Networks
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

A GAN, or Generative Adversarial Network, is a generative model that simultaneously trains two models: a generative model GG that captures the data distribution, and a discriminative model DD that estimates the probability that a sample came from the training data rather than GG.

The training procedure for GG is to maximize the probability of DD making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions GG and DD, a unique solution exists, with GG recovering the training data distribution and DD equal to 12\frac{1}{2} everywhere. In the case where GG and DD are defined by multilayer perceptrons, the entire system can be trained with backpropagation.

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