$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise

This repository is the official pytorch code of the $\epsilon$-Softmax [NeurIPS2024] https://openreview.net/pdf?id=vjsd8Bcipv

How to use

🔵 We simplify $\epsilon$-softmax with CE and FL by ECE and EFL in the code.

Benchmark Datasets: The running file is main.py

• dataset: cifar10 | cifar100, etc.
• loss: ECEandMAE, EFLandMAE, CE, GCE, etc.
• noise_type: symmetric | asymmetric | dependent (instance-dependent noise), etc.

CE $_\epsilon$+MAE (Semi): The running file is main_semi.py

• dataset: cifar10 | cifar100.
• noise_type: human (cifar-n dataset), etc.

Real-World Datasets: The running file is main_real_world.py

• dataset: webvision | clothing1m.
• loss: ECEandMAE, EFLandMAE, CE, GCE, etc.

Examples

ECEandMAE for cifar10 0.8 symmetric noise:

python3 main.py --dataset cifar10 --noise_type symmetric --noise_rate 0.8 --loss ECEandMAE    

ECEandMAE(Semi) for cifar10 human (cifar-n dataset) worst:

python3 main_semi.py --dataset cifar10 --noise_type human --noise_rate worst  

ECEandMAE for webvision:

python3 main_real_world.py --dataset webvision --loss ECEandMAE

Reference

For details, please check the paper. If you have used our method or code in your own, please consider citing:

@inproceedings{wang2024epsilonsoftmax,
  title={$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise},
  author={Jialiang, Wang and Xiong, Zhou and Deming, Zhai and Junjun, Jiang and Xiangyang, Ji and Xianming, Liu},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
  year={2024}
}

If you have any question, you can contact cswjl@stu.hit.edu.cn