Meta-Weight-Net

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Official Pytorch implementation for noisy labels). The implementation of class imbalance is available at https://github.com/xjtushujun/Meta-weight-net_class-imbalance.

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This is the code for the paper: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng* To be presented at NeurIPS 2019.

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

@inproceedings{han2018coteaching,
  title={Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting},
  author={Shu, Jun and Xie, Qi and Yi, Lixuan and Zhao, Qian and Zhou, Sanping and Xu, Zongben and Meng, Deyu},
  booktitle={NeurIPS},
  year={2019}
}

Setups

The requiring environment is as bellow:

Linux
Python 3+
PyTorch 0.4.0
Torchvision 0.2.0

Running Meta-Weight-Net on benchmark datasets (CIFAR-10 and CIFAR-100).

Here is an example:

python train_WRN-28-10_Meta_PGC.py --dataset cifar10 --corruption_type unif --corruption_prob 0.6

The default network structure is WRN-28-10, if you want to train with ResNet32 model, please reset the learning rate delay policy.

Acknowledgements

We thank the Pytorch implementation on glc(https://github.com/mmazeika/glc) and learning-to-reweight-examples(https://github.com/danieltan07/learning-to-reweight-examples).

Contact: Jun Shu (xjtushujun@gmail.com); Deyu Meng(dymeng@mail.xjtu.edu.cn).

jxw2018/meta-weight-net

Meta-Weight-Net

Setups

Running Meta-Weight-Net on benchmark datasets (CIFAR-10 and CIFAR-100).

Acknowledgements