Transcription of Generative Adversarial Nets
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Generative Adversarial Nets
Generative Adversarial NetsIan J. Goodfellow, Jean Pouget-Abadie , Mehdi Mirza, Bing Xu, David Warde-Farley,Sherjil Ozair , Aaron Courville, Yoshua Bengio D epartement d informatique et de recherche op erationnelleUniversit e de Montr ealMontr eal, QC H3C 3J7 AbstractWe propose a new framework for estimating Generative models via an adversar-ial process, in which we simultaneously train two models: a Generative modelGthat captures the data distribution, and a discriminative modelDthat estimatesthe probability that a sample came from the training data rather thanG. The train-ing procedure forGis to maximize the probability ofDmaking a mistake. Thisframework corresponds to a minimax two-player game. In the space of arbitraryfunctionsGandD, a unique solution exists, withGrecovering the training datadistribution andDequal to12everywhere.
g), where Gis a differentiable function represented by a multilayer perceptron with parameters g. We also define a second multilayer perceptron D(x; d) that outputs a single scalar. D(x) represents the probability that x came from the data rather than p g. We train Dto maximize the probability of assigning the
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