Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for training generative models. Two networks, a generator and a discriminator, are trained in an adversarial manner. The generator tries to create realistic data samples to fool the discriminator, while the discriminator tries to distinguish between real and generated samples. Through this competition, both networks improve, leading to a generator that can produce highly realistic images or other data.