AAAI'21: Learning to Purify Noisy Labels via Meta Soft Label Corrector (Official Pytorch implementation for noisy labels).
This is the code for the paper: Learning to Purify Noisy Labels via Meta Soft Label Corrector
Yichen Wu, Jun Shu, Qi Xie, Qian Zhao, Deyu Meng* To be presented at AAAI 2021.
If you find this code useful in your research then please cite
@article{wu2020learning,
title={Learning to Purify Noisy Labels via Meta Soft Label Corrector},
author={Wu, Yichen and Shu, Jun and Xie, Qi and Zhao, Qian and Meng, Deyu},
journal={arXiv preprint arXiv:2008.00627},
year={2020}
}
The requiring environment is as bellow:
- Linux
- Python 3+
- PyTorch 0.4.0
- Torchvision 0.2.0
Here is an example:
python main.py --dataset cifar10 --corruption_type unif(flip2) --corruption_prob 0.6
The default network structure is Resnet34
We thank the Pytorch implementation on glc(https://github.com/mmazeika/glc) and learning-to-reweight-examples(https://github.com/danieltan07/learning-to-reweight-examples).
Contact: Yichen Wu (wuyichen.am97@gmail.com); Deyu Meng(dymeng@mail.xjtu.edu.cn).