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[NeurIPS 2023] CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels

Code release for CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels (NeurIPS 2023).

[paper] [project page]

Requirements

• Python 3.7+
• PyTorch 1.8.0
• GPU Memory 24+ GB

We have conducted our experiments on a single GPU of NVIDIA A100 with 80 GB memory. We follow DivideMix and NCE to construct our codebase.

Getting started

• Modify data_path in main_cifar.py
• Train with command line

  CUDA_VISIBLE_DEVICES=0 python main_cifar.py
  

Cite our work

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

@inproceedings{
    chang2023csot,
    title={{CSOT}: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels},
    author={Wanxing Chang and Ye Shi and Jingya Wang},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
    year={2023},
    url={https://openreview.net/forum?id=y50AnAbKp1}
}