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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.

outputs different. The output in question is a single scalar. In GANs, one network produces a rich, high dimensional vector that is used as the input to another network, and attempts to choose an input that the other network does not know how to process. 3) The specification of the learning process is different.

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