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 of G is to fool D, and the two models are trained in a minimax game. This framework can be used to generate realistic images and other types of data.