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:
| Dataset | Method | Sym-20% | Sym-50% | Asym | Dependent | Open |
|---|---|---|---|---|---|---|
| CIFAR-10 | Ours | 91.06 | 82.50 | 90.00 | 85.37 | 91.47 |
| – | \eta | 2.5 | 2.5 | 3.0 | 3.5 | 2.0 |
| CIFAR-100 | Ours | 68.82 | 54.08 | 58.61 | 62.45 | 66.95 |
| – | \eta | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 |
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}
}
