Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for training generative models. Two neural networks compete with each other in a game. A generator network creates data samples, and a discriminator network tries to distinguish between the generated samples and real data. Through this adversarial process, the generator learns to produce increasingly realistic data.