Generative Adversarial Nets

This paper introduces a new framework for estimating generative models via an adversarial process, in which two models are trained simultaneously: 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 goal is to train the generative model to fool the discriminator, so both models improve until the discriminator cannot distinguish between real and generated samples.