This repository is the official implementation of Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels (BMVC 2021).

Authors: Filipe R. Cordeiro; Vasileios Belagiannis, Ian Reid and Gustavo Carneiro

Illustration

• This codebase is written for python3.
• To install necessary python packages, run pip install -r requirements.txt.

The pipeline for training with PropMix is the following:

1. Self-supervised pretrain.

In our paper we use SimCLR for most of the datasets. We use moco-v2 to pre-train an InceptionResNetV2 on Webvision. Other self-supervised methods can be used as well.

If you use SimCLR, run:

python simclr.py --config_env configs/env.yml --config_exp configs/pretext/<config_file.yml> --cudaid 0

2. Clustering

python scan.py --config_env configs/env.yml --config_exp configs/scan/<config_file.yml> --cudaid 0

3. Train the model (using the pretraining from steps 1 and 2)

For CIFAR-10/CIFAR-100:

python propmix.py --r [0.2/0.5/0.8/0.9] --noise_mode [sym/asym] --config_env configs/env.yml --config_exp configs/propmix/<config_file.yml> --cudaid 0

Add --nopt if you wish to train from scratch, without the self-supervised pretrain, from steps 1 and 2. Add --strong_aug to use strong augmentation. Recommended for high noise rates.

• This README is formatted based on paperswithcode.
• This project is licensed under the terms of the MIT license.
• Feel free to post issues via Github.

Cite PropMix
If you find the code useful in your research, please consider citing our paper:

@inproceedings{DBLP:conf/bmvc/CordeiroB0C21,
  author    = {Filipe Rolim Cordeiro and
               Vasileios Belagiannis and
               Ian Reid and
               Gustavo Carneiro},
  title     = {PropMix: Hard Sample Filtering and Proportional MixUp for Learning
               with Noisy Labels},
  booktitle = {32nd British Machine Vision Conference 2021, {BMVC} 2021, Online,
               November 22-25, 2021},
  pages     = {187},
  publisher = {{BMVA} Press},
  year      = {2021},
  url       = {https://www.bmvc2021-virtualconference.com/assets/papers/0908.pdf},
  timestamp = {Wed, 22 Jun 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/bmvc/CordeiroB0C21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Please contact filipe.rolim@ufrpe.br if you have any question on the codes.