Paper Daily: StyleGAN
A style-Based Generator Architecture for Generative Adversarial Networks The authors 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, and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of … Continue reading Paper Daily: StyleGAN