Joint Optimization Framework for Learning with Noisy Labels

This is an unofficial PyTorch implementation of Joint Optimization Framework for Learning with Noisy Labels. The official Chainer implementation is here.

Requirements

• Python 3.6
• PyTorch 0.4
• torchvision
• progress
• matplotlib
• numpy

Usage

Train the network on the Symmmetric Noise CIFAR-10 dataset (noise rate = 0.7):

First,

python train.py --gpu 0 --out first_sn07 --lr 0.08 --alpha 1.2 --beta 0.8 --percent 0.7

to train and relabel the dataset.

Secondly,

python retrain.py --gpu 0 --out second_sn07 --label first_sn07

to retrain on the relabeled dataset.

Train the network on the Asymmmetric Noise CIFAR-10 dataset (noise rate = 0.4):

First,

python train.py --gpu 0 --out first_an04 --lr 0.03 --alpha 0.8 --beta 0.4 --percent 0.4 --asym

to train and relabel the dataset.

Secondly,

python retrain.py --gpu 0 --out second_an04 --label first_an04

to retrain on the relabeled dataset.

References

• D. Tanaka, D. Ikami, T. Yamasaki and K. Aizawa. "Joint Optimization Framework for Learning with Noisy Labels", in CVPR, 2018.