Awesome

PENCIL.pytorch

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

Requirements:

python3.6
numpy
torch-0.4.1
torchvision-0.2.0

Usage

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

options

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

The framework of PENCIL

<div align="center"> <img src="https://github.com/yikun2019/PENCIL/blob/master/framework.png" height="336" width="652" > </div> ## The proportion of correct labels on CIFAR-10 <div align="center"> <img src="https://github.com/yikun2019/PENCIL/blob/master/SN70.png" height="360" width="480" > <img src="https://github.com/yikun2019/PENCIL/blob/master/AN30.png" height="360" width="480" > </div>

The results on real-world dataset Clothing1M

#	method	Test Accuracy (%)
1	Cross Entropy Loss	68.94
2	Forward [1]	69.84
3	Tanaka et al. [2]	72.16
4	PENCIL	73.49

Citing this repository

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}
}

Reference

[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. </br>[2] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In CVPR, pages 5552–5560, 2018.