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Generative Adversarial Nets - NIPS

Generative Adversarial Nets Ian J. Goodfellow , Jean Pouget-Abadie , mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair , Aaron Courville, Yoshua Bengio . De partement d'informatique et de recherche ope rationnelle Universite de Montre al Montre al, QC H3C 3J7. Abstract We propose a new framework for estimating Generative models via an adversar- ial 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 train- ing 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 21 everywhere.

Ian J. Goodfellow, Jean Pouget-Abadiey, Mehdi Mirza, Bing Xu, David Warde-Farley, ... the potential of the framework through qualitative and quantitative evaluation of the generated samples. ... Ian Goodfellow is now a research scientist at Google, but did this work earlier as a UdeM student ...

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