StyleGAN is a type of generative adversarial network. It uses an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature; in particular, the use of adaptive instance normalization.

It synthesizes artificial examples, such as pictures that are obscure from authentic photographs. A typical example of a GAN application is to produce artificial face pictures by learning from a dataset of notable faces.