https://github.com/Justherozen/ProMix

[IJCAI 2023] ProMix: Combating Label Noise via Maximizing Clean Sample Utility
https://github.com/Justherozen/ProMix

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[IJCAI 2023] ProMix: Combating Label Noise via Maximizing Clean Sample Utility

• Host: GitHub
• URL: https://github.com/Justherozen/ProMix
• Owner: Justherozen
• License: mit
• Created: 2022-06-27T18:32:05.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-10T02:12:58.000Z (6 months ago)
• Last Synced: 2024-08-02T15:30:05.590Z (3 months ago)
• Language: Python
• Homepage:
• Size: 1.69 MB
• Stars: 75
• Watchers: 3
• Forks: 10
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# ProMix: Combating Label Noise via Maximizing Clean Sample Utility

This is the [PyTorch](http://pytorch.org/) implementation of our IJCAI 2023 paper [ProMix](https://arxiv.org/abs/2207.10276). A previous version at the `LMNL_challenge` branch won the [1st Learning and Mining with Noisy Labels Challenge](http://competition.noisylabels.com/) in IJCAI-ECAI 2022.

**Title:** ProMix: Combating Label Noise via Maximizing Clean Sample Utility

**Authors:** Ruixuan Xiao, Dong Yiwen, Haobo Wang, Lei Feng, Runze Wu, Gang Chen, Junbo Zhao

**Affliations:** Zhejiang University, Nanyang Technological University, NetEase Fuxi AI Lab

```

@inproceedings{ijcai2023p494,

  title     = {ProMix: Combating Label Noise via Maximizing Clean Sample Utility},

  author    = {Xiao, Ruixuan and Dong, Yiwen and Wang, Haobo and Feng, Lei and Wu, Runze and Chen, Gang and Zhao, Junbo},

  booktitle = {Proceedings of the Thirty-Second International Joint Conference on

               Artificial Intelligence, {IJCAI-23}},

  publisher = {International Joint Conferences on Artificial Intelligence Organization},

  editor    = {Edith Elkind},

  pages     = {4442--4450},

  year      = {2023},

  month     = {8},

  note      = {Main Track},

  doi       = {10.24963/ijcai.2023/494},

  url       = {https://doi.org/10.24963/ijcai.2023/494},

}

```

### Framework

![Framework](./resources/framework.png)

### Main Results on CIFAR-10/100

![result_cf](./resources/result_cf.png)

### Main Results on CIFAR-N

![result_cfn](./resources/result_cfn.png)

### Usage

After creating a virtual environment, run

```

pip install -r requirements.txt

```

We provide the shell codes for model training in the `run.sh` file. Please download the source data of CIFAR-10/100 and the noise file of CIFAR-N following [Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations](https://github.com/UCSC-REAL/cifar-10-100n) and put them under the `data` folder.

###  Acknowledgement

This paper is supported by [Netease Youling Crowdsourcing Platform](https://fuxi.163.com). As the importance of data continues rising, Netease Youling Crowdsourcing Platform is dedicated to utilizing various advanced algorithms to provide high-quality, low-noise labeled samples. Feel free to contact us for more information.