Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for training generative models. Two networks, a generator and a discriminator, are trained in competition. The generator creates new data instances, while the discriminator evaluates them for authenticity. This adversarial process pushes both networks to improve, resulting in the generator producing increasingly realistic data.