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A Second-Order Approach to Learning with Instance-Dependent Label Noise (CVPR'21 oral)
This code is a PyTorch implementation of the paper "A Second-Order Approach to Learning with Instance-Dependent Label Noise" accepted by CVPR 2021 as oral presentation.
Prerequisites
Python 3.6.9
PyTorch 1.4.0
Torchvision 0.5.0
Instructions
Run the code:
CIFAR10:
python run_exptPRLD_C10_CAL.py
CIFAR100:
python run_exptPRLD_C100_CAL.py
The following changes also apply to CIFAR100.
Run the code step-by-step
Step-1: Construct $\hat D$:
Modify Lines 27-34 of run_exptPRLD_C10_CAL.py as:
#-------------- customized parameters --------------#
noise_rate = 0.6 # noise rates = 0.2, 0.4, 0.6
lossfunc = "crossentropy" # use this lossfunc for constructing D
# lossfunc = "crossentropy_CAL" # use this lossfunc for CAL
gpu_idx = "0" # Choose one GPU index
#---------------------------------------------------#
Step-2: Train CAL:
Modify Lines 27-34 of run_exptPRLD_C10_CAL.py as:
#-------------- customized parameters --------------#
noise_rate = 0.6 # noise rates = 0.2, 0.4, 0.6
# lossfunc = "crossentropy" # use this lossfunc for constructing D
lossfunc = "crossentropy_CAL" # use this lossfunc for CAL
gpu_idx = "0" # Choose one GPU index
#---------------------------------------------------#
Citation
If you find this code useful, please cite the following paper:
@inproceedings{zhu2021second,
title={A second-order approach to learning with instance-dependent label noise},
author={Zhu, Zhaowei and Liu, Tongliang and Liu, Yang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10113--10123},
year={2021}
}
Corresponding authors:
Dr. Zhaowei Zhu: zwzhu@ucsc.edu
Prof. Yang Liu: yangliu@ucsc.edu