Generative Adversarial Networks

Generative Adversarial Networks (GANs) is a type of generative model that uses an adversarial process to estimate the data distribution. It consists of two models: a generative model G and a discriminative model D, which are trained simultaneously through a minimax two-player game. The training procedure for G is to maximize the probability of D making a mistake, while D estimates the probability that a sample came from the training data rather than G. This framework allows for the generation of new samples that resemble the training data distribution.