Open-set Label Noise Can Improve Robustness Against Inherent Label Noise

NeurIPS 2021: This repository is the official implementation of ODNL.

Requirements

To install requirements:

pip install -r requirements.txt

Training

To train the model(s) in the paper, run this command:

python train.py cifar10 --alg odnl -m wrn --noise_type symmetric --noise_rate 0.4 --exp_name test --gpu 0 --lambda_o 3.0

Evaluation

To evaluate the model on CIFAR-10, run:

python test.py cifar10 --method_name cifar10_symmetric_04_wrn_test_odnl --num_to_avg 10 --gpu 0 --seed 1 --prefetch 0 --out_as_pos

Hyperparameter

The best test accuracy (%) and the value of \eta on CIFAR-10/100 using vanilla ODNL is shown as follow:

Datasets

You can download 300K Random Images datasets (from OE) in the following url:

300K Random Images

Citation

If you find this useful in your research, please consider citing:

@article{wei2021odnl,
  title={Open-set Label Noise Can Improve Robustness Against Inherent Label Noise},
  author={Wei, Hongxin and Tao, Lue and Xie, Renchunzi and An, Bo},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2021}
}