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README.md

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

@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:

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 (xingrui.yu@student.uts.edu.au); Bo Han (bo.han@riken.jp).

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

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