5,337
edits
StyleGAN: Difference between revisions
→Adaptive Instance Normalization
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An affine layer (fully connected with no activation, block A in the figure) converts the style to a mean <math>y_{b,i}</math> and standard deviation <math>y_{s,i}</math>.<br> | An affine layer (fully connected with no activation, block A in the figure) converts the style to a mean <math>y_{b,i}</math> and standard deviation <math>y_{s,i}</math>.<br> | ||
Then the feature map is shifted and scaled to have this mean and standard deviation.<br> | Then the feature map is shifted and scaled to have this mean and standard deviation.<br> | ||
* <math>\operatorname{AdaIN(\mathbf{x}_i, \mathbf{y}) = \mathbf{y}_{s,i}\frac{\mathbf{x}_i - \mu(\mathbf{x}_i)}{\sigma(\mathbf{x}_i)} + \mathbf{y}_{b,i}</math> | * <math>\operatorname{AdaIN}(\mathbf{x}_i, \mathbf{y}) = \mathbf{y}_{s,i}\frac{\mathbf{x}_i - \mu(\mathbf{x}_i)}{\sigma(\mathbf{x}_i)} + \mathbf{y}_{b,i}</math> | ||
==Results== | ==Results== | ||