Generative adversarial nets

Generative Adversarial Networks (GANs) are a framework for training generative models by pitting two neural networks against each other: a generator that tries to create realistic data and a discriminator that tries to distinguish between real and generated data. This adversarial process drives both networks to improve, ultimately leading the generator to produce data that is indistinguishable from real data.