Winning Ticket in Noisy Image Classification

This repository is the official implementation of "Winning Ticket in Noisy Classificaition" paper.

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.
• To install necessary python packages, run pip install -r requirements.txt.

Training

DivideMix

• Most codes are similar with the original implementation code in https://github.com/LiJunnan1992/DivideMix. So, if you want to run the baseline model, just run the baseline.sh file in the dividemix folder.
• If you want to train the model with either CLK or SAME, move to the folder dividemix and run the bash files by following the README.md file in the dividemix folder.

Robust loss and Co-teaching

• Most codes are similar with the original implementation code in https://github.com/bhanML/Co-teaching and https://github.com/shengliu66/ELR. If you want to run the baseline models, run the robust_loss_baseline.sh file or co-teaching_baseline.sh file.
• If you want to train the model with either CLK or SAME, move to the folder robust_loss_coteaching and run the bash files by following the README.md file in the robust_loss_coteaching 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.