# tri-training

**Repository Path**: tri-trianing/tri-training

## Basic Information

- **Project Name**: tri-training
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-11-04
- **Last Updated**: 2021-12-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://arxiv.org/abs/1805.08193)

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](https://arxiv.org/abs/1804.06872)  
Bo Han*, Quanming Yao*, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama  
To be presented at [NeurIPS 2018](https://nips.cc/Conferences/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: 

```bash
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](https://arxiv.org/abs/1911.02377) [code](https://github.com/AutoML-4Paradigm/S2E)