Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for training generative models by setting up a two-player game. A generator network tries to create realistic data samples, while a discriminator network tries to distinguish between real and generated samples. Through this adversarial process, both networks improve, leading the generator to produce increasingly realistic outputs. This approach provides a novel way to train generative models without requiring complex Markov chains or intractable inference procedures, making it applicable to various types of data generation tasks, including image synthesis.