Abstract
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. The new generator improves the state-of-the-art
in terms of traditional distribution quality metrics, leads to
demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify
interpolation quality and disentanglement, we propose two
new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied
and high-quality dataset of human faces.