Instance Normalization (also known as contrast normalization) is a normalization layer where:
$$ y_{tijk} = \frac{x_{tijk} - \mu_{ti}}{\sqrt{\sigma_{ti}^2 + \epsilon}}, \quad \mu_{ti} = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H x_{tilm}, \quad \sigma_{ti}^2 = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H (x_{tilm} - \mu_{ti})^2. $$
This prevents instance-specific mean and covariance shift simplifying the learning process. Intuitively, the normalization process allows to remove instance-specific contrast information from the content image in a task like image stylization, which simplifies generation.
Source: Instance Normalization: The Missing Ingredient for Fast StylizationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Translation | 105 | 10.71% |
Image-to-Image Translation | 91 | 9.29% |
Image Generation | 53 | 5.41% |
Style Transfer | 50 | 5.10% |
Domain Adaptation | 47 | 4.80% |
Semantic Segmentation | 46 | 4.69% |
Unsupervised Domain Adaptation | 21 | 2.14% |
Image Segmentation | 21 | 2.14% |
Object Detection | 20 | 2.04% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |