We propose an alternative generator architecture for
generative adversarial networks, borrowing from style
transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level
attributes (e.g., pose and identity when trained on human
faces) and stochastic variation in the generated images
(e.g., freckles, hair), and it enables intuitive, scale-specific
control of the synthesis.