Awesome

<p align="center"> <h2 align="center"><strong>Dataset Distillers Are Good Label Denoisers In the Wild</strong></h2> <p align="center"> <span> <a href="https://scholar.google.com/citations?user=PKFAv-cAAAAJ&hl=en">Lechao Cheng</a>, Kaifeng Chen, Jiyang Li, Shengeng Tang, Shufei Zhang, Meng Wang </span> </p> <div align="center"> <a href='https://arxiv.org/abs/2411.11924'><img src='https://img.shields.io/badge/arXiv-2411.11924-b31b1b.svg'></a> </div> </div>

Getting Started

Environment

You can create environment as follows

conda env create -f environment.yaml
conda activate ddlnl

Dataset

For Tiny-ImageNet, it is best to download it in tiny-imagenet-200 and then process it through dataSolu/deal_Tiny.py.

Train

See ddlnl_scripts.md to reproduce the results

Acknowledgement

Our code is built upon DATM, DANCE and RCIG.

Citation

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

@article{cheng2024dataset,
  title={Dataset Distillers Are Good Label Denoisers In the Wild},
  author={Cheng, Lechao and Chen, Kaifeng and Li, Jiyang and Tang, Shengeng and Zhang, Shufei and Wang, Meng},
  journal={arXiv preprint arXiv:2411.11924},
  year={2024}
}