Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework where two neural networks compete: a generator creates new data instances, and a discriminator evaluates them for authenticity. This adversarial process pushes both networks to improve, with the generator producing increasingly realistic data and the discriminator becoming better at distinguishing real from fake. GANs have become a foundational technique for generating novel content, including images, by learning the underlying distribution of training data.