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NoiLIn

The code for NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels (TMLR 22 accept)

Requirements

  • Python (3.8)
  • PyTorch (1.11)
  • CUDA (11.3)
  • AutoAttack (This is a package for implementing AutoAttack. You can install it via pip install git+https://github.com/fra31/auto-attack)
  • Numpy

Conducting experiments

(1) SAT-NoiLIn/TRADES-NoiLIn using ResNet18/Wide ResNet on CIFAR10/CIAFR100/SVHN

python SAT-NoiLIn.py --dataset='dataset' --noise_type='noise_type' --net='net_name'
python TRADES-NoiLIn.py --dataset='dataset' --noise_type='noise_type' --net='net_name'

(2) TRADES-AWP-NoiLIn using WRN-34-10 on CIFAR10

cd TRADES-AWP-NoiLIn
python TRADES-AWP-NoiLIn.py

(3) SAT-NoiLIn with extra training data using WRN-28-10 on CIFAR10
You need to download ti_500K_pseudo_labeled.pickle which contains 500K pseudo-labeled TinyImages from this link (Auxillary data provided by Carmon et al. 2019). Then, store ti_500K_pseudo_labeled.pickle into the folder ./data. In addition, we recommend to using mutilple GPUs.

cd NoiLIn_ExtraData
python SAT-NoiLIn-ExtraData.py --gpu='0,1,2,3' --aux_data_filename='ti_500K_pseudo_labeled.pickle'

(4) Evaluate the performance of trained models
(4.1) Obtain the learning curve of natural and robust accuracy

python eval.py --all_epoch --start_epoch=1 --end_epoch=120 --model_dir='model_dir'

(4.2) Obtain natural and robust accuracy of a given model

python eval.py --model_dir='model_dir' --pt_name='model_pt_name'

Reference

@article{
zhang2022noilin,
title={NoiLin: Improving adversarial training and correcting stereotype of noisy labels },
author={Jingfeng Zhang and Xilie Xu and Bo Han and Tongliang Liu and Lizhen Cui and Gang Niu and Masashi Sugiyama},
journal={Transactions on Machine Learning Research},
year={2022},
url={https://openreview.net/forum?id=zlQXV7xtZs},
}

Contact

Please contact jingfeng.zhang@riken.jp and xuxilie@comp.nus.edu.sg if you have any questions on the codes.