This repository is the official implementation of Generalized Data Weighting via Class-level Gradient Manipulation (NeurIPS 2021).

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

@article{chen2021generalized,
  title={Generalized DataWeighting via Class-Level Gradient Manipulation},
  author={Chen, Can and Zheng, Shuhao and Chen, Xi and Dong, Erqun and Liu, Xue Steve and Liu, Hao and Dou, Dejing},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={14097--14109},
  year={2021}
}

• Linux
• Python 3.7
• Pytorch 1.9.0
• Torchvision 0.9.1

More specifically, run this command:

pip install -r requirements.txt

Download CIFAR10 and place it in ./data.

To compare mw-net and gdw on CIFAR10 under 40% uniform noise, run this command:

python -u  main.py --corruption_prob 0.4 --dataset cifar10 --mode mw-net --outer_lr 100
python -u  main.py --corruption_prob 0.4 --dataset cifar10 --mode gdw --outer_lr 100

We set the outer level learning as 100 on CIFAR10 and 1000 on CIFAR100.

We place training logs of the above command in ./log and list results as below:

We thank the Pytorch implementation on mw-net(https://github.com/xjtushujun/meta-weight-net).