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 StyleGANPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image Generation | 52 | 19.33% |
Disentanglement | 13 | 4.83% |
Image Manipulation | 13 | 4.83% |
Face Generation | 11 | 4.09% |
Face Recognition | 8 | 2.97% |
Translation | 7 | 2.60% |
Conditional Image Generation | 7 | 2.60% |
Face Swapping | 6 | 2.23% |
Domain Adaptation | 6 | 2.23% |
Component | Type |
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Convolution
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Convolutions | |
Leaky ReLU
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Activation Functions | |
Path Length Regularization
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Regularization | |
R1 Regularization
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Regularization | |
Weight Demodulation
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Normalization |