Dataset Factorization

This is the pytorch implementation of the following NeurIPS 2022 paper:

Dataset Distillation via Factorization

Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, and Xinchao Wang.

Installation

• Create a new environment if you want:

  conda create -n HaBa python=3.8
  conda activate HaBa

• Clone the repo and install the required packages:

  git clone https://github.com/Huage001/DatasetFactorization.git
  cd DatasetFactorization
  pip install -r requirements.txt

Dataset Distillation

• Install required packages:

  pip install -r requirements.txt

• First, generate buffer of training trajectories using:

  python buffer.py --dataset=CIFAR10 --model=ConvNet --train_epochs=50 --num_experts=100 --zca --buffer_path={path_to_buffer_storage} --data_path={path_to_dataset}

• Then, edit run_cifar10_ipc[xx]_style5.sh. Change {path_to_buffer_storage} to your path of buffers and {path_to_dataset} to your path of datasets.

• Run:

  bash run_cifar10_ipc[xx]_style5.sh

  [xx] can be 1, 10, or 50.

• Most of hyper-parameters are following the baseline repo. You may also try other configurations of arguments in the .sh files freely.

• distill.py contains the original implementation of the baseline method MTT for comparison.

Acknowledgement

This code borrows heavily from mtt-distillation and DatasetCondensation.

Citation

If you find this project useful in your research, please consider cite our paper and the default baseline method:

@article{liu2022dataset,
    author    = {Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, Xinchao Wang},
    title     = {Dataset Distillation via Factorization},
    journal   = {NeurIPS},
    year      = {2022},
}

@inproceedings{
cazenavette2022distillation,
title={Dataset Distillation by Matching Training Trajectories},
author={George Cazenavette and Tongzhou Wang and Antonio Torralba and Alexei A. Efros and Jun-Yan Zhu},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2022}
}