This code is a PyTorch implementation of the paper:
Zhaowei Zhu, Yiwen Song, and Yang Liu, "Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels," https://proceedings.mlr.press/v139/zhu21e.html.
Python 3.6.6
PyTorch 1.3.0
Torchvision 0.4.1
Datasets will be downloaded to ./data/.
On CIFAR-10 with instance 0.6 noise.
export CUDA_VISIBLE_DEVICES=0 && nohup python -u main.py --pre_type image --dataset cifar10 --loss fw --label_file_path ./data/IDN_0.6_C10_0.pt> ./out/test10.out &
On CIFAR-10 with real-world human-annotated labels
export CUDA_VISIBLE_DEVICES=0 && nohup python -u main.py --pre_type image --dataset cifar10 --loss fw --label_file_path ./data/noise_label_human.pt> ./out/test10.out &
On CIFAR-100 with instance 0.6 noise.
export CUDA_VISIBLE_DEVICES=1 && nohup python -u main.py --pre_type image --dataset cifar100 --loss fw --label_file_path ./data/IDN_0.6_C100_0.pt> ./out/test100.out &
We collected them from Amazon Mechanical Turk (MTurk) and students at UC Santa Cruz in February 2020. We only collected one annotation for each image at the cost of ¢10 per image. The label file is available at ./data/noise_label_human.pt.
G: the number of rounds needed to estimate the consensus probabilities (See details in Algorithm 1 [1]) max_iter: the maximum number of iterations to get an estimate of T
CUDA_VISIBLE_DEVICES=0 python main_min.py --G 50 --max_iter 1500
@InProceedings{zhu2021clusterability,
title = {Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels},
author = {Zhu, Zhaowei and Song, Yiwen and Liu, Yang},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {12912--12923},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/zhu21e/zhu21e.pdf},
url = {https://proceedings.mlr.press/v139/zhu21e.html}
}
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