Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework where two neural networks contest with each other in a game. One network, the generator, creates data (like images), and the other, the discriminator, evaluates it. The generator aims to fool the discriminator, while the discriminator tries to distinguish between real and generated data. Through this competition, both networks improve, leading the generator to produce increasingly realistic data.