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Generative Adversarial Networks
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Abstract: We propose a new framework for estimating generative models via an
adversarial process, in which we simultaneously train 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 soluti ...
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Yuval Filmus
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FrankS
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Ken
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