This repository contains the code for the paper Joint Optimization Framework for Learning with Noisy Labels.
- Python 3.6
- Chainer 4.0.0
- CuPy 4.0.0
- ChainerCV 0.9.0
To train the network on the Symmmetric Noise CIFAR-10 dataset (noise rate = 0.7):
$ python first_step_train.py --gpu 0 --out first_sn07 --learnrate 0.08 --alpha 1.2 --beta 0.8 --percent 0.7
$ python second_step_train.py --gpu 0 --out second_sn07 --label first_sn07
To train the network on the Asymmmetric Noise CIFAR-10 dataset (noise rate = 0.4):
$ python first_step_train.py --gpu 0 --out first_an04 --learnrate 0.03 --alpha 0.8 --beta 0.4 --percent 0.4 --asym
$ python second_step_train.py --gpu 0 --out second_an04 --label first_an04
@inproceedings{tanaka2018joint,
title = {Joint Optimization Framework for Learning with Noisy Labels},
author = {Tanaka, Daiki and Ikami, Daiki and Yamasaki, Toshihiko and Aizawa, Kiyoharu},
booktitle = {CVPR},
year = {2018}
}