Transcription of Generative Adversarial Nets - NIPS
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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.
value function V(G;D): min G max D V(D;G) = E x˘p data(x)[logD(x)]+E z˘p z(z)[log(1 D(G(z)))]: (1) In the next section, we present a theoretical analysis of adversarial nets, essentially showing that the training criterion allows one to recover the data generating distribution as Gand Dare given enough capacity, i.e., in the non-parametric limit.
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