Generative Adversarial Nets

Generative Adversarial Networks (GANs) provide a framework for training generative models by pitting two networks against each other: a generator that creates new data instances, and a discriminator that evaluates them for authenticity. This adversarial process allows both networks to improve, leading the generator to produce increasingly realistic outputs that can fool the discriminator.