Introduction to Generative Adversarial Networks

Generative adversarial networks are deep models that learn to generate samples drawn from the same distribution as the training data. As with many deep generative models, the log-likelihood for a GAN is intractable. Unlike most other models, GANs do not require Monte Carlo or variational methods to overcome this intractability. Instead, GANs are trained by seeking a Nash equilibrium in a game played between a discriminator network that attempts to distinguish real data from model samples and a generator network that attempts to fool the discriminator. Stable algorithms for finding Nash equilibria remain an important research direction. Like many other models, GANs can also be applied to semi-supervised learning.