Need help with Co-teaching?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

bhanML
273 Stars 76 Forks 35 Commits 5 Opened issues

Description

NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

Services available

!
?

Need anything else?

Contributors list

# 283,073
Python
Shell
20 commits
# 371,014
Python
Shell
8 commits
# 385,716
Shell
MATLAB
C
factori...
3 commits

Co-teaching

NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels (Pytorch implementation).

Another related work in NeurIPS'18:

Masking: A New Perspective of Noisy Supervision

Code available: https://github.com/bhanML/Masking

========

This is the code for the paper: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama
To be presented at NeurIPS 2018.

If you find this code useful in your research then please cite

bash
@inproceedings{han2018coteaching,
  title={Co-teaching: Robust training of deep neural networks with extremely noisy labels},
  author={Han, Bo and Yao, Quanming and Yu, Xingrui and Niu, Gang and Xu, Miao and Hu, Weihua and Tsang, Ivor and Sugiyama, Masashi},
  booktitle={NeurIPS},
  pages={8535--8545},
  year={2018}
}

Setups

All code was developed and tested on a single machine equiped with a NVIDIA K80 GPU. The environment is as bellow:

  • CentOS 7.2
  • CUDA 8.0
  • Python 2.7.12 (Anaconda 4.1.1 64 bit)
  • PyTorch 0.3.0.post4
  • numpy 1.14.2

Install PyTorch via:

bash
pip install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp27-cp27mu-linux_x86_64.whl

Running Co-teaching on benchmark datasets (MNIST, CIFAR-10 and CIFAR-100)

Here is an example:

python main.py --dataset cifar10 --noise_type symmetric --noise_rate 0.5 

Performance

| (Flipping, Rate) | MNIST | CIFAR-10 | CIFAR-100 | | ---------------: | -----: | -------: | --------: | | (Pair, 45%) | 87.58% | 72.85% | 34.40% | | (Symmetry, 50%) | 91.68% | 74.49% | 41.23% | | (Symmetry, 20%) | 97.71% | 82.18% | 54.36% |

Contact: Xingrui Yu ([email protected]); Bo Han ([email protected]).

AutoML

Please check the automated machine learning (AutoML) version of Co-teaching in - Searching to Exploit Memorization Effect in Learning from Corrupted Labels. ICML-2020 paper code

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.