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https://github.com/jinxins/adversarial-automixup
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https://github.com/jinxins/adversarial-automixup
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- Host: GitHub
- URL: https://github.com/jinxins/adversarial-automixup
- Owner: JinXins
- Created: 2023-11-24T07:20:29.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-19T14:45:54.000Z (about 1 year ago)
- Last Synced: 2023-12-19T17:16:00.366Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 49 MB
- Stars: 6
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Mixup - [Code
README
Adversarial AutoMixup (ICLR 2024 spotlight)
[Huafeng Qin](https://scholar.google.com/citations?user=5jvXcJ0AAAAJ&hl=zh-CN)1,\*,†, [Xin Jin](https://scholar.google.com/citations?user=v3OwxWIAAAAJ&hl=zh-CN)1,\*, Yun Jiang1, [Mounim A. El-Yacoubi](https://scholar.google.com/citations?user=ObFYefYAAAAJ&hl=zh-CN)2, [Xinbo Gao](https://scholar.google.com/citations?user=VZVTOOIAAAAJ&hl=zh-CN&oi=sra)3
1[Chongqing Technology and Business University](https://www.ctbu.edu.cn/)
2[Telecom SudParis, Institut Polytechnique de Paris](https://www.ip-paris.fr/telecom-sudparis)
3[Chongqing University of Posts and Telecommunications](https://www.cqupt.edu.cn/)
* Equal Contribution † Corresponding Author
We propose **AdAutoMix**, an adversarial automatic mixup augmentation approach that generates challenging samples to train a robust classifier for image classification, by alternatively optimizing the classifier and the mixup sample generator. AdAutoMix comprises two modules, a mixed example generator, and a target classifier. The mixed sample generator aims to produce hard mixed examples to challenge the target classifier while the target classifier’s aim is to learn robust features from hard mixed examples to improve generalization. To prevent the collapse of the inherent meanings of images, we further introduce an exponential moving average (EMA) teacher and cosine similarity to train AdAutoMix in an end-to-end way.
### Mixed Images of Various Mixup-based Approaches.
### 📬 You can contact me by email: [email protected] or WeChat: *xinxinxinxin_j*.
**If you are interested in *palm or finger vein research*, please contact us!**
___
## 🛠 Installation
***💥News! ! !💥***
***2024-01-18:*** *you can clone Openmixup training **"AdAutoMix"[[ICLR 2024]](https://arxiv.org/abs/2312.11954)***.
***2023-12-19:*** *We update some analysis tools code such as: Calibration, FGSM* `calibration_fgsm.py` *and Occlusion Robustness* `occlusion_robustness.py` *experiments, also we support a mix augmentation method **"SnapMix"[[AAAI 2021]](https://arxiv.org/abs/2012.04846)***.
*Big thanks to **Siyuan Li[(@Lupin1998)](https://github.com/Lupin1998)***.
***🔧How to install?🔧***
*In fact, you can add our python file in **OpenMixup***.
*There, you can see how to use it and the environment required. What you need to do is add or replace our files by folder inside OpenMixup, and then add the function names of the files in the `__init__.py` file*.
*You also can download or find other Mixup methods in **OpenMixup("https://github.com/Westlake-AI/openmixup")***
*Thanks contributors: **Siyuan Li[(@Lupin1998)](https://github.com/Lupin1998), Zichen Liu[(@pon7)](https://github.com/pone7) and Zedong Wang[(@Jacky1128)](https://github.com/Jacky1128)***.
___
**Here are the commands to install OpenMixup**
```markdown
conda create -n openmixup python=3.8 pytorch=1.12 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate openmixup
pip install openmim
mim install mmcv-full
git clone https://github.com/Westlake-AI/openmixup.git
cd openmixup
python setup.py develop
```
**Here are the commands to git clone AdAutoMixup**
```markdown
git clone https://github.com/JinXins/Adversarial-AutoMixup.git
```
___
## 📊 Experiments
### CIFAR-100
| Name | alpha | Conference | ResNet18 | ResNeXt50 | Swin-Tiny | ConvNeXt-T |
|------------------|-------|------------|----------|-----------|-----------|------------|
| Vanilla | - | | 78.04 | 81.09 | 78.41 | 78.70 |
| [MixUp](https://arxiv.org/abs/1710.09412) | 1.0 | ICLR2018 | 79.12 | 82.10 | 76.78 | 81.13 |
| [CutMix](https://arxiv.org/abs/1905.04899) | 0.2 | ICCV2019 | 78.17 | 78.32 | 80.64 | 82.46 |
| [SaliencyMix](https://arxiv.org/abs/2006.01791) | 0.2 | ICLR2021 | 79.12 | 78.77 | 80.40 | 82.82 |
| [FMix](https://arxiv.org/abs/2002.12047) | 0.2 | ArXiv | 79.69 | 79.02 | 80.72 | 81.79 |
| [ResizeMix](https://arxiv.org/abs/2012.11101) | 1.0 | CVMJ2023 | 80.01 | 80.35 | 80.16 | 82.53 |
| [PuzzleMix](https://arxiv.org/abs/2009.06962) | 1.0 | ICML2020 | 81.13 | 82.85 | 80.33 | 82.29 |
| [AutoMix](https://arxiv.org/abs/2103.13027) | 2.0 | ECCV2022 | 82.04 | 83.64 | 82.67 | 83.30 |
| [AdAutoMix](https://arxiv.org/abs/2312.11954) | 1.0 | ICLR2024 |**82.32**|**84.22** |**84.33**|**83.54**|
___
### Tiny-ImageNet & ImageNet-1K(denote *)
| Name | alpha | Conference | ResNet18 | ResNeXt50 | ResNet18* | ResNet34* | ResNet50* |
|------------------|-------|------------|----------|-----------|----------|----------|----------|
| Vanilla | - | | 61.68 | 65.04 | 70.04 | 73.85 | 76.83 |
| MixUp | 1.0 | ICLR2018 | 63.86 | 66.36 | 69.98 | 73.97 | 77.12 |
| CutMix | 0.2 | ICCV2019 | 65.53 | 66.47 | 68.95 | 73.58 | 77.17 |
| SaliencyMix | 0.2 | ICLR2021 | 64.40 | 66.55 | 69.16 | 73.56 | 77.14 |
| FMix | 0.2 | ArXiv | 63.47 | 65.08 | 69.96 | 74.08 | 77.19 |
| ResizeMix | 1.0 | CVMJ2023 | 63.17 | 65.87 | 69.50 | 73.88 | 77.42 |
| PuzzleMix | 1.0 | ICML2020 | 65.81 | 67.83 | 70.12 | 74.26 | 77.54 |
| AutoMix | 2.0 | ECCV2022 | 67.33 | 70.72 | 70.50 | 74.52 | 77.91 |
| AdAutoMix | 1.0 | ICLR2024 | **69.19**| **72.89** | **70.86**| **74.82**| **78.04**|
___
### CUB-200, FGVC-Aircraft and Standford Cars
| Name | alpha | Confrence | CUB R18 | CUB R50 | FGVC R18 | FGVC RX50 | Cars R18 | Cars RX50 |
|------------------|-------|------------|---------|---------|--------------------|---------------------|--------------------|---------------------|
| Vanilla | - | | 77.68 | 82.38 | 80.23 | 85.1 | 86.32 | 90.15 |
| MixUp | 1.0 | ICLR2018 | 78.39 | 82.98 | 79.52 | 85.18 | 86.27 | 90.81 |
| CutMix | 0.2 | ICCV2019 | 78.40 | 83.17 | 78.84 | 84.55 | 87.48 | 91.22 |
| ManifoldMixup | 2.0 | ICML2019 | 79.76 | 83.76 | 80.68 | 86.6 | 85.88 | 90.20 |
| SaliencyMix | 0.2 | ICLR2021 | 77.95 | 82.02 | 80.02 | 84.31 | 86.48 | 90.60 |
| FMix | 0.2 | ArXiv | 77.28 | 83.34 | 79.36 | 86.23 | 87.55 | 90.90 |
| ResizeMix | 1.0 | CVMJ2023 | 78.5 | 83.41 | 78.1 | 84.08 | 88.17 | 91.36 |
| PuzzleMix | 1.0 | ICML2020 | 78.63 | 83.83 | 80.76 | 86.23 | 87.78 | 91.29 |
| AutoMix | 2.0 | ECCV2022 | 79.87 | 83.88 | 81.37 | 86.72 | 88.89 | 91.38 |
| AdAutoMix | 1.0 | ICLR2024 |**80.88**|**84.57**| **81.73** | **87.16** | **89.19** | **91.59** |
**If you want see more results, please check this [Experiments.md](Experiments.md).**
___
## 😉 Citation
**If you feel that our work has contributed to your research, please cite it, 🥰 and please don`t forget to cite OpenMixup if you use this project ! 🤗 Thanks.**
```markdown
@inproceedings{iclr2024adautomix,
title={Adversarial AutoMixup},
author={Huafeng Qin and Xin Jin and Yun Jiang and Mounim A. El-Yacoubi and Xinbo Gao},
booktitle={International Conference on Learning Representations},
year={2024},
}
@article{li2022openmixup,
title = {OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification},
author = {Siyuan Li and Zedong Wang and Zicheng Liu and Di Wu and Cheng Tan and Stan Z. Li},
journal = {ArXiv},
year = {2022},
volume = {abs/2209.04851}
}
```
___
### 📝 Interest
*If you are interested in vein research (palm veins, finger veins, etc.), why not contact us and we will be happy to discuss the research or questions with you.
Of course, we're also trying to research how we can use AI to predict and detect diseases, potentially Alzheimer's, Parkinson's, etc., but of course, this is new research for us, so we're still figuring it out.*
Email: [email protected]