instancenorm

**InstanceNorm** (Instance Normalization) is a **normalization technique that normalizes each feature map of each sample independently** — computing mean and variance per channel per instance, widely used in neural style transfer and image generation. **How Does InstanceNorm Work?** - **Scope**: Normalize over $H imes W$ spatial dimensions for each channel of each sample independently. - **Formula**: $hat{x}_{nchw} = (x_{nchw} - mu_{nc}) / sqrt{sigma_{nc}^2 + epsilon}$ - **No Batch**: Statistics computed per-instance, per-channel. Completely batch-independent. - **Paper**: Ulyanov et al. (2016). **Why It Matters** - **Style Transfer**: Removes instance-specific contrast information -> enables style transfer (AdaIN). - **Image Generation**: Used in StyleGAN and other generative models for controlling per-instance statistics. - **Equivalence**: InstanceNorm = GroupNorm with $G = C$ (one channel per group). **InstanceNorm** is **per-image, per-channel normalization** — the normalization of choice for style transfer and image generation tasks.