Generative Adversarial Nets

This paper introduces generative adversarial networks (GANs), a new framework for estimating generative models. Two neural networks compete in a game: a generator that creates new data instances and a discriminator that evaluates them for authenticity. The generator tries to fool the discriminator, while the discriminator tries to identify the fake instances. Through this adversarial process, the generator learns to produce realistic data samples.