Co-teaching

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

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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 NIPS 2018.

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

@inproceedings{Han2018CoteachingRT,
  title={Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels},
  author={Bo Han and Quanming Yao and Xingrui Yu and Gang Niu and Miao Xu and Weihua Hu and Ivor W. Tsang and Masashi Sugiyama},
  booktitle={NIPS},
  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).