Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for estimating generative models via an adversarial process, where two networks, a generator and a discriminator, compete. The generator produces new data instances, while the discriminator evaluates them for authenticity. Through iterative training, the generator learns to produce increasingly realistic data, and the discriminator becomes better at distinguishing real from generated data, ultimately leading to a generative model that can create highly realistic samples. This framework provides a novel way to train generative models without Markov chains or unrolled approximate inference networks.