Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels (ICML'21)

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.

Demo

http://peers.ai/

Prerequisites

Python 3.6.6

PyTorch 1.3.0

Torchvision 0.4.1

Datasets will be downloaded to ./data/.

Run HOC + forward loss correction

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 &

Real-world human-annotated CIFAR-10

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.

Minimal implementation of HOC

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

Reference

@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}
}