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, while the discriminator tries to distinguish between real and generated data. Through this process, the generator learns to produce data that is indistinguishable from real data, which is applicable to graphic design, video editing, and other creative tasks.