Awesome
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
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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 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