/Co-learning

The official implementation of the ACM MM'21 paper Co-learning: Learning from noisy labels with self-supervision.

Primary LanguagePython

Co-learning: Learning from noisy labels with self-supervision

This repository contains a unified framework for co-training-based noisy label learning methods.

The official implementation of the paper Co-learning: Learning from noisy labels with self-supervision is also included.


Introduction

Supported algorithms
Supported datasets:
  • CIFAR-10
  • CIFAR-100
Supported synthetic noise types:
  • 'sym' (Symmetric noisy labels)
  • 'asym' (Asymmetric noisy labels)
  • 'ins' (Instance-dependent noisy labels)

Dependency

  • numpy
  • torch, torchvision
  • scipy
  • addict
  • matplotlib

Citation

If you are interested in our repository and our paper, please cite the following paper:

@inproceedings{tan2021co,
  title={Co-learning: Learning from noisy labels with self-supervision},
  author={Tan, Cheng and Xia, Jun and Wu, Lirong and Li, Stan Z},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={1405--1413},
  year={2021}
}