NLNL-Negative-Learning-for-Noisy-Labels
Pytorch implementation for paper NLNL: Negative Learning for Noisy Labels, ICCV 2019
Paper: https://arxiv.org/abs/1908.07387
Requirements
- python3
- pytorch
- matplotlib
Generating noisy data
python3 noise_generator.py --noise_type val_split_symm_exc
Start training
Simply run sh file: run.sh
GPU=0 setting='--dataset cifar10_wo_val --model resnet34 --noise 0.2 --noise_type val_split_symm_exc'
CUDA_VISIBLE_DEVICES=$GPU python3 main_NL.py $setting
CUDA_VISIBLE_DEVICES=$GPU python3 main_PL.py $setting --max_epochs 720
CUDA_VISIBLE_DEVICES=$GPU python3 main_pseudo1.py $setting --lr 0.1 --max_epochs 480 --epoch_step 192 288
CUDA_VISIBLE_DEVICES=$GPU python3 main_pseudo2.py $setting --lr 0.1 --max_epochs 480 --epoch_step 192 288
Citation
@inproceedings{kim2019nlnl,
title={Nlnl: Negative learning for noisy labels},
author={Kim, Youngdong and Yim, Junho and Yun, Juseung and Kim, Junmo},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={101--110},
year={2019}
}