Download

To download our collected noisy labels, click the link below:

import torch
# For CIFAR-10N noisy labels
noise_label = torch.load('CIFAR-10_human.pt')
clean_label = noise_label['clean_label']
worst_label = noise_label['worse_label']
aggre_label = noise_label['aggre_label']
random_label1 = noise_label['random_label1']
random_label2 = noise_label['random_label2']
random_label3 = noise_label['random_label3']
# For CIFAR-100N noisy labels
noise_label = torch.load('CIFAR-100_human.pt')
clean_label = noise_label['clean_label']
noisy_label = noise_label['noisy_label']

For more details, i.e., the starter code and tensorflow dataloader, please see this Github, or click the above Github icon.

Reference

If you use this dataset of our reproduced results, please cite:

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations.
Jiaheng Wei*, Zhaowei Zhu*, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu. (*: equal contributions)

The BibTex infomation is detached as:
@inproceedings{
wei2022learning,
title={Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations},
author={Jiaheng Wei and Zhaowei Zhu and Hao Cheng and Tongliang Liu and Gang Niu and Yang Liu},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=TBWA6PLJZQm}
}

Contact

Please contact us via {yangliu, jiahengwei, zwzhu, haocheng}@ucsc.edu, if you have any concerns regarding this dataset.