Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for estimating generative models via an adversarial process. 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 generator tries to fool the discriminator, and the discriminator tries to identify fake samples. This adversarial process drives both models to improve until the generator's output is indistinguishable from real data.