README.md

RTM_LNL

Regularly Truncated M-estimators for Learning with Noisy Labels (PyTorch implementation).

This is the code for the paper: Regularly Truncated M-estimators for Learning with Noisy Labels
Xiaobo Xia*, Pengqian Lu*, Chen Gong, Bo Han, Jun Yu, Jun Yu, and Tongliang Liu

Dependencies

torch==1.10.0

tqdm==4.64.1

numpy==1.23.0

torchvision==0.11.0

Experiments

To run the experiments for rtcatoni on mnist with 30% symmetric label noise, run

python main.py --loss rtcatoni --noise_type symmetric --noise_rate 30 --dataset mnist

result will be saved in ./results/results.csv

The loss options for our method is rtcatoni, rtwelschp, rtlogsum.

The noise type options is symmetric, pairflip, ILN.

The noise rate options is 30, 50 but 45 for pairflip.

Rurrently, we have release the code on four datasets, including mnsit, cifar10, cifar100, svhn and news.

If you find this code useful in your research, please consider citing:

@article{xia2023regularly,
  title={Regularly truncated m-estimators for learning with noisy labels},
  author={Xia, Xiaobo and Lu, Pengqian and Gong, Chen and Han, Bo and Yu, Jun and Liu, Tongliang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023},
  publisher={IEEE}
}