The official implementation of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration
You only need to replace the BN with our RBN without any other adjustment.
- 2022.7.7 RBN is verified useful for multimodal learning.
- 2021.4.9 The Jittor implementation is available now in
Jittor. - 2021.4.1 The training code of ImageNet classification using RBN is released.
Batch Normalization (BatchNorm) has become the default component in modern neural networks to stabilize training. In BatchNorm, centering and scaling operations, along with mean and variance statistics, are utilized for feature standardization over the batch dimension. The batch dependency of BatchNorm enables stable training and better representation of the network, while inevitably ignores the representation differences among instances. We propose to add a simple yet effective feature calibration scheme into the centering and scaling operations of BatchNorm, enhancing the instance-specific representations with the negligible computational cost. The centering calibration strengthens informative features and reduces noisy features. The scaling calibration restricts the feature intensity to form a more stable feature distribution. Our proposed variant of BatchNorm, namely Representative BatchNorm, can be plugged into existing methods to boost the performance of various tasks such as classification, detection, and segmentation.
The training code of ImageNet classification is released in ImageNet_training folder.
If you find this work or code is helpful in your research, please cite:
@inproceedings{gao2021rbn,
title={Representative Batch Normalization with Feature Calibration},
author={Gao, Shang-Hua and Han, Qi and Li, Duo and Peng, Pai and Cheng, Ming-Ming and Pai Peng},
booktitle=CVPR,
year={2021}
}
The source code is free for research and education use only. Any comercial use should get formal permission first.
If you have any questions, feel free to E-mail Shang-Hua Gao (shgao(at)live.com) and Qi Han(hqer(at)foxmail.com).