NeurIPS 2021, "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.
We refer to some official implementation codes
python3
(used python 3.7.6
while implementing).pip install -r requirements.txt
.FINE
, move to the folder dynamic_selection
and run the bash files by following the README.md
.FINE
(f-dividemix
), move to the folder dividemix
and run the bash files by following the README.md
in the dividemix
folder.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.
License
This project is licensed under the terms of the MIT license.
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].