This repository is the official implementation of Asymmetric Loss Functions for Learning with Noisy Labels [ICML 2021] and Asymmetric Loss Functions for Noise-tolerant Learning: Theory and Applications [T-PAMI].
Python >= 3.6, PyTorch >= 1.3.1, torchvision >= 0.4.1, numpy>=1.11.2, tqdm >= 4.50.2, seaborn >= 0.11.0, tensorboardX >= 2.5The main running file is main.py with arguments as follows:
- noise_type: symmetric | asymmetric
- noise_rate: noise rate
- loss: AGCE | AUL | AEL | CE (Cross Entropy) | FL (Focal Loss) | MAE | GCE | SCE | NFL | NCE | ...
The detailed implementation about the proposed asymmetric losses for classification can be found in ./lnl/losses.py
Example for 0.4 Symmetric noise rate with AUL loss
# CIFAR-10
$ python3 main.py --noise_type symmetric \
--noise_rate 0.4 \
--loss AUL \The main running file is main.py with arguments as follows:
- exp: n2c | n2n | n2s
- style: gauss | bernoulli | saltpepper | impulse
- loss: heat | poisson | lp | mse ...
The detailed implementation about the proposed asymmetric losses for regression can be found in ./denoising/losses.py
Example for using the negative heat kernel loss for Gaussian denoising with noise2self
$ python3 main.py --exp n2s \
--style gauss15 \
--loss heat0.1 \For technical details and full experimental results, please check the paper. If you have used our work in your own, please consider citing:
@InProceedings{zhou2021asymmetric,
title = {Asymmetric Loss Functions for Learning with Noisy Labels},
author = {Zhou, Xiong and Liu, Xianming and Jiang, Junjun and Gao, Xin and Ji, Xiangyang},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {12846--12856},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR}
}
@ARTICLE{10039708,
author={Zhou, Xiong and Liu, Xianming and Zhai, Deming and Jiang, Junjun and Ji, Xiangyang},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Asymmetric Loss Functions for Noise-Tolerant Learning: Theory and Applications},
year={2023},
volume={},
number={},
pages={1-16},
doi={10.1109/TPAMI.2023.3236459}
}
Moreover, we thank the code implemented by Ma et al. (classification) and Zhang et al. (DnCNN).
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