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

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


To install requirements:

pip install -r requirements.txt


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

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


To evaluate the model on CIFAR-10, run:

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


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


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

300K Random Images


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

  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)},


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