Nvidia uses Progressive Growing of GANs for Improved Quality, Stability, and Variation and makes photorealistic faces with them
We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively, starting from low-resolution images, and add new layers that deal with higher resolution details as the training progresses. This greatly stabilizes the training and allows us to produce images of unprecedented quality, e.g., Read more about Nvidia uses Progressive Growing of GANs for Improved Quality, Stability, and Variation and makes photorealistic faces with them[…]