Switchable Normalization for Learning-to-Normalize Deep Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Ping LuoJingyu Li

Abstract

We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learning their importance weights in an end-to-end manner. It has several good properties. First, it adapts to various network architectures and tasks (see Fig. 1). Second, it is robust to a wide range of batch sizes, maintaining high performance even when small minibatch is presented (e.g., 2 images/GPU). Third, SN does not have sensitive hyper-parameter, unlike group normalization that searches the number of groups as a hyper-parameter. Without bells and whistles, SN outperforms its counterparts on various challenging benchmarks, such as ImageNet, COCO, CityScapes, ADE20K, MegaFace and Kinetics. Analyses of SN are also presented to answer the following three questions: (a) Is it useful to allow each normalization layer to select its own normalizer? (b) What impacts the choices of normalizers? (c) Do different tasks and datasets prefer different normalizers? We hope SN will help ease the ...Continue Reading

Citations

Aug 7, 2021·Frontiers in Neurorobotics·Zhikui ChenJianing Zhang

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Software Mentioned

ENAS
GN
SN
Detectron
COCO
ArcFace
MegaFace
SyncSN
BN
Faster R - CNN

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