Awesome
SANM
Source code for our CVPR paper Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
Learning with Noisy labels via Self-supervised Adversarial Noisy Masking (CVPR 2023)
This is the pytorch implementation of the paper (accepted by CVPR 2023).
<img src='SANM.png'>Fig 1.SANM framework
Training
First you need to install dependencies by running pip install -r requirements.txt
.
Then, please create a folder named <i>checkpoint</i> to store the results.
mkdir checkpoint
Next, run
python Train_{dataset_name}.py --data_path <i>path-to-your-data</i>
Performance
<img src='Clothing1m.png'>Videos
For the introduction of the paper, you can refer to bilibili or youtube for more details.
Citation
If you find SANM useful in your research, please consider citing.
@inproceedings{tu2023learning,
title={Learning with Noisy labels via Self-supervised Adversarial Noisy Masking},
author={Tu, Yuanpeng and Zhang, Boshen and Li, Yuxi and Liu, Liang and Li, Jian and Zhang, Jiangning and Wang, Yabiao and Wang, Chengjie and Zhao, Cai Rong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={16186--16195},
year={2023}
}
Reference
For C2D and DivideMix, you can refer to C2D and DivideMix and combine them with our SANM. Thanks for their great work!