Group Normalization

European Conference on Computer Vision (ECCV)


Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems — BN’s error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN’s usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN’s computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code.

Related Publications

All Publications

LEEP: A New Measure to Evaluate Transferability of Learned Representations

Cuong V. Nguyen, Tal Hassner, Matthias Seeger, Cedric Archambeau

ICML - July 13, 2020

Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels

Yi Zhou, Chenglei Wu, Zimo Li, Chen Cao, Yuting Ye, Jason Saragih, Hao Li, Yaser Sheikh

arXiv - July 1, 2020

Passthrough+: Real-time Stereoscopic View Synthesis for Mobile Mixed Reality

Gaurav Chaurasia, Arthur Nieuwoudt, Alexandru-Eugen Ichim, Richard Szeliski, Alexander Sorkine-Hornung

I3D - April 14, 2020

Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation

Edoardo Remelli, Shangchen Han, Sina Honari, Pascal Fua, Robert Wang

CVPR - June 16, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy