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.
In the space of arbitrary functions Gand D, a unique solution exists, with Grecovering the training data ... specification of a probability distribution function. The model can then be trained by maximiz-ing the log likelihood. In this family of model, perhaps the most succesful is the deep Boltzmann ... The generator Gimplicitly defines a ...
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