Generative Models


Introduced by Karras et al. in Analyzing and Improving the Image Quality of StyleGAN

StyleGAN2 is a generative adversarial network that builds on StyleGAN with several improvements. First, adaptive instance normalization is redesigned and replaced with a normalization technique called weight demodulation. Secondly, an improved training scheme upon progressively growing is introduced, which achieves the same goal - training starts by focusing on low-resolution images and then progressively shifts focus to higher and higher resolutions - without changing the network topology during training. Additionally, new types of regularization like lazy regularization and path length regularization are proposed.

Source: Analyzing and Improving the Image Quality of StyleGAN


Paper Code Results Date Stars


Task Papers Share
Image Generation 51 19.39%
Disentanglement 13 4.94%
Image Manipulation 13 4.94%
Face Generation 11 4.18%
Translation 7 2.66%
Face Recognition 7 2.66%
Conditional Image Generation 7 2.66%
Face Swapping 6 2.28%
Domain Adaptation 6 2.28%