Comments (1)
For the style-based generator (which will feed the latent code to all convolutional layers), SeFa supports analyzing the code for each layer independently. But, indeed, we only focus on the first mapping layer (fully-connected). Exploring the function of convolutional layers as well as considering the non-linear activation is worth exploring in the future. Thanks for the suggestion.
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Related Issues (20)
- reproduce results matched figure 5 in paper HOT 1
- About attribute predictor and re-scoring analysis. HOT 5
- Compability issue for .pkl extension HOT 2
- StyleGAN2 LSUN bedroom HOT 1
- Direction indices for Closed Form applied on PGGAN HOT 1
- Edit face in Eyeglasses, Gender, Hair Color, Pose , Smile ... direction failed HOT 1
- I can't run this scripts
- StyleGAN3 Support HOT 1
- Annotations corresponding to eigen vectors
- StyleGAN 2 ADA transfer learning sourcenet error HOT 1
- Help -- factorize_weight --
- streetscapes stylegan 2 model
- Size mismatch error
- EOFError: Ran out of input HOT 1
- Some questions about your code HOT 3
- BigGAN HOT 4
- Question about factorize_weight() HOT 1
- About ignore the non-linear layers of the mapping net. HOT 4
- how to directly use checkpoint from stylegan2-ada-pytorch HOT 3
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