Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for training generative models. Two networks, a generator and a discriminator, are trained in competition. The generator creates samples, while the discriminator tries to distinguish between real and generated samples. This adversarial process pushes the generator to produce increasingly realistic outputs, which can be used for tasks like image generation and manipulation, forming the base of tools like Mokker AI.