Generalized Data Weighting via Class-level Gradient Manipulation

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:

@misc{chen2021generalized,
      title={Generalized Data Weighting via Class-level Gradient Manipulation}, 
      author={Can Chen and Shuhao Zheng and Xi Chen and Erqun Dong and Xue Liu and Hao Liu and Dejing Dou},
      year={2021},
      eprint={2111.00056},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Requirements

• Linux
• Python 3.7
• Pytorch 1.9.0
• Torchvision 0.9.1

More specifically, run this command:

pip install -r requirements.txt

Run mw-net and gdw on CIFAR10

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.

Results

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

Acknowledgements

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