Distilling Long-tailed Datasets

Paper

Existing DD methods exhibit degraded performance when applied to imbalanced datasets, especially when the imbalance factor increases, whereas our method provides significantly better performance under different imbalanced scenarios.

Getting Started

1. Create environment as follows

conda env create -f environment.yaml
conda activate distillation

2. Generate expert trajectories

cd buffer

# representation experts
python buffer_FTD.py --cfg ../configs/buffer/CIFAR10_LT/imbrate_0005/first_stage_weight_balance.yaml

# classifier experts
python buffer_FTD.py --cfg ../configs/buffer/CIFAR10_LT/imbrate_0005/second_stage_weight_balance.yaml

3. Perform the distillation

cd distill
python EDGE_tesla.py --cfg ../configs/xxxx.yaml

Acknowledgement

Our code is built upon MTT, FTD, and DATM.

Citation

If you find our code useful for your research, please cite our paper.

@article{zhao2024distilling,
  title={Distilling Long-tailed Datasets},
  author={Zhao, Zhenghao and Wang, Haoxuan and Shang, Yuzhang and Wang, Kai and Yan, Yan},
  journal={arXiv preprint arXiv:2408.14506},
  year={2024}
}