LiBiying / Meta-weight-net_class-imbalance

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for class imbalance).

Meta-Weight-Net

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

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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 meta-weight-net-class-imbalance.py --dataset cifar100 --num_classes 100 --imb_factor 0.01

Acknowledgements

We thank the Pytorch implementation on class-balanced-loss(https://github.com/richardaecn/class-balanced-loss) and learning-to-reweight-examples(https://github.com/danieltan07/learning-to-reweight-examples).

Contact: Jun Shu ([e-mail]); Deyu Meng([e-mail]).