Normalization
Adaptive Instance Normalization
Introduced by Huang et al. in Arbitrary Style Transfer in Real-time with Adaptive Instance NormalizationAdaptive Instance Normalization is a normalization method that aligns the mean and variance of the content features with those of the style features.
Instance Normalization normalizes the input to a single style specified by the affine parameters. Adaptive Instance Normaliation is an extension. In AdaIN, we receive a content input $x$ and a style input $y$, and we simply align the channel-wise mean and variance of $x$ to match those of $y$. Unlike Batch Normalization, Instance Normalization or Conditional Instance Normalization, AdaIN has no learnable affine parameters. Instead, it adaptively computes the affine parameters from the style input:
$$ \textrm{AdaIN}(x, y)= \sigma(y)\left(\frac{x-\mu(x)}{\sigma(x)}\right)+\mu(y) $$
Source: Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Generation | 75 | 13.13% |
| Disentanglement | 34 | 5.95% |
| Style Transfer | 25 | 4.38% |
| Image-to-Image Translation | 23 | 4.03% |
| Image Manipulation | 20 | 3.50% |
| Face Generation | 18 | 3.15% |
| Face Recognition | 17 | 2.98% |
| Translation | 16 | 2.80% |
| Face Swapping | 14 | 2.45% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
