Instance-Level Meta Normalization

Songhao Jia, Ding-Jie Chen, Hwann-Tzong Chen; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4865-4873

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.

Related Material


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[bibtex]
@InProceedings{Jia_2019_CVPR,
author = {Jia, Songhao and Chen, Ding-Jie and Chen, Hwann-Tzong},
title = {Instance-Level Meta Normalization},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}