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[Official] FINE Samples for Learning with Noisy Labels

This repository is the official implementation of "FINE Samples for Learning with Noisy Labels" paper presented in NeurIPS 2021. New version of previous repository https://github.com/jaychoi12/FINE. Future code modifications and official developments will take place here. Thanks to the contributors in the previous repo.

• Paper, NeurIPS 21, FINE Samples for Learning with Noisy Labels, [Arxiv][OpenReview]

Reference Codes

We refer to some official implementation codes

• https://github.com/bhanML/Co-teaching

• https://github.com/LiJunnan1992/DivideMix

• https://github.com/shengliu66/ELR

Requirements

• This codebase is written for python3 (used python 3.7.6 while implementing).
• To install necessary python packages, run pip install -r requirements.txt.

Training

Sample-Selection Approaches and Collaboration with Noise-Robust loss functions

• Most code strucutres are similar with the original implementation code in https://github.com/bhanML/Co-teaching and https://github.com/shengliu66/ELR.
• If you want to train the model with FINE, move to the folder dynamic_selection and run the bash files by following the README.md.

Semi-Supervised Approaches

• Most codes are similar with the original implementation code in https://github.com/LiJunnan1992/DivideMix.
• If you want to train the model with FINE (f-dividemix), move to the folder dividemix and run the bash files by following the README.md in the dividemix folder.

Results

You can reproduce all results in the paper with our code. All results have been described in our paper including Appendix. The results of our experiments are so numerous that it is difficult to post everything here. However, if you experiment several times by modifying the hyperparameter value in the .sh file, you will be able to reproduce all of our analysis.

Contact

• Jongwoo Ko : Jongwoo.ko@kaist.ac.kr
• Taehyeon Kim : potter32@kaist.ac.kr

<b>License</b>
This project is licensed under the terms of the MIT license.

Acknowledgements

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) [No.2019-0-00075, Artificial Intelligence Graduate School Program (KAIST)] and [No. 2021-0-00907, Development of Adaptive and Lightweight Edge-Collaborative Analysis Technology for Enabling Proactively Immediate Response and Rapid Learning].