NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels.
bhanML/Co-teaching is ranked 17,833 with 521 stars.
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Key parameters and specifications
Key parameters and specifications| Parameter | Value |
|---|
| Language | Python |
|---|
| Category | Model Development |
|---|
| Subcategory | Deep Learning Frameworks |
|---|
| Stars | 521 |
|---|
| Forks | 109 |
|---|
| Contributors | 3 |
|---|
| Created date | 2018-10-17 |
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| Updated date | 2026-10-12 |
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| Stars in 1 day | 0 |
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| Stars in 7 days | 1 |
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| Rank | 17,833 |
|---|
| Previous rank | 17,617 |
|---|
| Rank change | -216 |
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| 7-day stars rank | 11,523 |
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| Previous 7-day stars rank | 11,579 |
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| 7-day stars rank change | 56 |
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| Total stars rank | 18,759 |
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| Previous total stars rank | 18,732 |
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| Total stars rank change | -27 |
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| Country | China |
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README summary
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: ======== 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 Setups All code was developed and tested on a single machine equiped with a NVIDIA K80 GPU. AutoML Please check the automated machine learning (AutoML) version of Co-teaching in Searching to Exploit Memorization Effect in Learning from Corrupted Labels.
Frequently asked questions
How many stars and forks are recorded?
bhanML/Co-teaching has 521 stars and 109 forks.
What is the repository rank?
bhanML/Co-teaching is ranked 17,833.
What language and category are recorded?
bhanML/Co-teaching has 521 stars, uses Python and belongs to Model Development.
Which repository is closest in rank?
bhanML/Co-teaching is compared with airalcorn2/Deep-Semantic-Similarity-Model, ranked 17,832 with 521 stars.
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