Instance-Level Meta Normalization

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

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.