Instance Normalization
Instance Normalization
**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.
Instance Normalization: The Missing Ingredient for Fast Stylization
https://paperswithcode.com/paper/instance-normalization-the-missing-ingredient
Instance Normalization: The Missing Ingredient for Fast Stylization