PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.

• python3.6
• numpy
• torch-0.4.1
• torchvision-0.2.0

• On CIFAR-10, we retained 10% of the CIFAR-10 training data as the validation set and modify the original correct labels to obtain different noisy label datasets.
• So the validation set is part of data_batch_5, and both of them have 5000 samples
• Add symmetric noise on CIFAR-10: python addnoise_SN.py
• Add asymmetric noise on CIFAR-10: python addnoise_AN.py
• PENCIL.py is used for both training a model on dataset with noisy labels and validating it

• b: batch size
• lr: initial learning rate of stage1
• lr2: initial learning rate of stage3
• alpha: the coefficient of Compatibility Loss
• beta: the coefficient of Entropy Loss
• lambda1: the value of lambda
• stage1: number of epochs utill the end of stage1
• stage2: number of epochs utill the end of stage2
• epoch: number of total epochs to run
• datanum: number of train dataset samples
• classnum: number of train dataset classes

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

@inproceedings{PENCIL_CVPR_2019,
author = {Kun, Yi and Jianxin, Wu},
title = {{Probabilistic End-to-end Noise Correction for Learning with Noisy Labels}},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019}
}

[1] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In CVPR, pages 1944–1952, 2017.
[2] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In CVPR, pages 5552–5560, 2018.