# Instance-Level Meta Normalization

## TL;DR

This paper introduces Instance-Level Meta Normalization (ILM Norm), a novel normalization method that predicts normalization parameters through feature and gradient paths, improving performance across various architectures and tasks.

## Contribution

ILM Norm is a new meta normalization mechanism that can be integrated into existing instance-level normalization schemes, enhancing their adaptability and performance.

## Key findings

- ILM Norm maintains high performance with small mini-batches.
- It adapts well to different network architectures.
- It consistently improves model performance.

## Abstract

This paper presents a normalization mechanism called Instance-Level Meta Normalization (ILM~Norm) to address a learning-to-normalize problem. ILM~Norm learns to predict the normalization parameters via both the feature feed-forward and the gradient back-propagation paths. ILM~Norm provides a meta normalization mechanism and has several good properties. It can be easily plugged into existing instance-level normalization schemes such as Instance Normalization, Layer Normalization, or Group Normalization. ILM~Norm normalizes each instance individually and therefore maintains high performance even when small mini-batch is used. The experimental results show that ILM~Norm well adapts to different network architectures and tasks, and it consistently improves the performance of the original models. The code is available at url{https://github.com/Gasoonjia/ILM-Norm.