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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.

Generative adversarial networks has been sometimes confused with the related concept of “adversar-ial examples” [28]. Adversarial examples are examples found by using gradient-based optimization directly on the input to a classification network, in order to find examples that are similar to the data yet misclassified.

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