LNL-NCE

A pytorch implementation for "Neighborhood Collective Estimation for Noisy Label Identification and Correction", accepted by ECCV2022. More details of this work can be found in our paper: Arxiv or ECCV2022.

Installation

Refer to DivideMix.

Model training

(1) To run training on CIFAR-10/CIFAR-100 with different noise modes (namely sym or asym) and various noise ratios (namely 0.20, 0.50, 0.80, 0.90, etc.),

CUDA_VISIBLE_DEVICES=0 python ./cifar/main.py --dataset cifar10 --num_class 10 --batch_size 128 --data_path ./data/cifar-10/ --r 0.50 --noise_mode sym --remark exp-ID

CUDA_VISIBLE_DEVICES=0 python ./cifar/main.py --dataset cifar100 --num_class 100 --batch_size 128 --data_path ./data/cifar-100/ --r 0.50 --noise_mode sym --remark exp-ID

(2) To run training on Webvision-1.0,

CUDA_VISIBLE_DEVICES=0,1,2 python ./webvision/main.py --data_path ./data/webvision/ --remark exp-ID

Citation

If you consider using this code or its derivatives, please consider citing:

@inproceedings{li2022neighborhood,
  title={Neighborhood Collective Estimation for Noisy Label Identification and Correction},
  author={Li, Jichang and Li, Guanbin and Liu, Feng and Yu, Yizhou},
  booktitle={European Conference on Computer Vision},
  pages={128--145},
  year={2022},
  organization={Springer}
}

Contact

Please feel free to contact the first author, namely Li Jichang, with an Email address li.jichang@foxmail.com, if you have any questions.