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https://github.com/amazon-science/crossnorm-selfnorm
CrossNorm and SelfNorm for Generalization under Distribution Shifts, ICCV 2021
https://github.com/amazon-science/crossnorm-selfnorm
computer-vision domain-adaptation domain-generalization iccv-2021 model-robustness natural-language-processing normalization
Last synced: 2 days ago
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CrossNorm and SelfNorm for Generalization under Distribution Shifts, ICCV 2021
- Host: GitHub
- URL: https://github.com/amazon-science/crossnorm-selfnorm
- Owner: amazon-science
- License: apache-2.0
- Created: 2021-08-13T16:40:01.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-09-10T22:27:16.000Z (about 3 years ago)
- Last Synced: 2023-10-19T19:57:58.833Z (about 1 year ago)
- Topics: computer-vision, domain-adaptation, domain-generalization, iccv-2021, model-robustness, natural-language-processing, normalization
- Language: Python
- Homepage:
- Size: 474 KB
- Stars: 111
- Watchers: 2
- Forks: 7
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
## CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021)
This is the official PyTorch implementation of our [CNSN paper](https://arxiv.org/abs/2102.02811), in which we propose CrossNorm (CN) and SelfNorm (SN), two simple, effective, and complementary normalization techniques to improve generalization robustness under distribution shifts.
```
@article{tang2021cnsn,
title={CrossNorm and SelfNorm for Generalization under Distribution Shifts},
author={Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris Metaxas},
journal={arXiv preprint arXiv:2102.02811},
year={2021}
}
```
## Install dependencies
```shell
conda create --name cnsn python=3.7
conda activate cnsn
conda install numpy
conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
```
## Prepare datasets
- Download CIFAR-10-C and CIFAR-100-C datasets with:
```
mkdir -p ./data
curl -O https://zenodo.org/record/2535967/files/CIFAR-10-C.tar
curl -O https://zenodo.org/record/3555552/files/CIFAR-100-C.tar
tar -xvf CIFAR-100-C.tar -C data/
tar -xvf CIFAR-10-C.tar -C data/
```
- Download ImageNet-C with:
```
mkdir -p ./data/ImageNet-C
curl -O https://zenodo.org/record/2235448/files/blur.tar
curl -O https://zenodo.org/record/2235448/files/digital.tar
curl -O https://zenodo.org/record/2235448/files/noise.tar
curl -O https://zenodo.org/record/2235448/files/weather.tar
tar -xvf blur.tar -C data/ImageNet-C
tar -xvf digital.tar -C data/ImageNet-C
tar -xvf noise.tar -C data/ImageNet-C
tar -xvf weather.tar -C data/ImageNet-C
```
## Usage
We have included sample scripts in `cifar10-scripts`, `cifar100-scripts`, and `imagenet-scripts`.
For example, there are 5 scripts for CIFAR-100 and WideResNet:
1. `./cifar100-scripts/wideresnet/run-cn.sh`
2. `./cifar100-scripts/wideresnet/run-sn.sh`
3. `./cifar100-scripts/wideresnet/run-cnsn.sh`
4. `./cifar100-scripts/wideresnet/run-cnsn-consist.sh` (Use CNSN with JSD consistency regularization)
5. `./cifar100-scripts/wideresnet/run-cnsn-augmix.sh` (Use CNSN with AugMix)
## Pretrained models
- Pretrained ResNet-50 ImageNet classifiers are available:
1. [ResNet-50 + CN](https://drive.google.com/file/d/1F-2uW1-zkPNg38PhxGmdYc8O3eow7IFG/view?usp=sharing)
2. [ResNet-50 + SN](https://drive.google.com/file/d/1ErWPFGUnkgOPKKjwgyNaAAocCWCw1R4I/view?usp=sharing)
3. [ResNet-50 + CNSN](https://drive.google.com/file/d/1Y-r2ET518jBwe2F-qG80glE32C6KQNHg/view?usp=sharing)
4. [ResNet-50 + CNSN + IBN + AugMix](https://drive.google.com/file/d/1Uov7bRZYY7q38FBXn3Q1HwWr8PnGKase/view?usp=sharing).
- Results of the above 4 ResNet-50 models on ImageNet:
| | +CN | +SN | +CNSN | +CNSN+IBN+AugMix |
| :-------- |:---------:| :----:| :----:| :---: |
| Top-1 err | 23.3 | 23.7 | 23.3 | 22.3 |
| mCE | 75.1 | 73.8 | 69.7 | 62.8 |