DD-Ranking: Rethinking the Evaluation of Dataset Distillation

In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance comparable to those trained on the original datasets. To further improve the performance of synthetic datasets, various training pipelines and optimization objectives have been proposed, greatly advancing the field of dataset distillation. Recent decoupled dataset distillation methods introduce soft labels and stronger data augmentation during the post-evaluation phase and scale dataset distillation up to larger datasets (e.g., ImageNet-1K). However, this raises a question: Is accuracy still a reliable metric to fairly evaluate dataset distillation methods? Our empirical findings suggest that the performance improvements of these methods often stem from additional techniques rather than the inherent quality of the images themselves, with even randomly sampled images achieving superior results. Such misaligned evaluation settings severely hinder the development of DD. Therefore, we propose DD-Ranking, a unified evaluation framework, along with new general evaluation metrics to uncover the true performance improvements achieved by different methods. By refocusing on the actual information enhancement of distilled datasets, DD-Ranking provides a more comprehensive and fair evaluation standard for future research advancements.

@article{li2025_2505.13300,
  title={ DD-Ranking: Rethinking the Evaluation of Dataset Distillation },
  author={ Zekai Li and Xinhao Zhong and Samir Khaki and Zhiyuan Liang and Yuhao Zhou and Mingjia Shi and Ziqiao Wang and Xuanlei Zhao and Wangbo Zhao and Ziheng Qin and Mengxuan Wu and Pengfei Zhou and Haonan Wang and David Junhao Zhang and Jia-Wei Liu and Shaobo Wang and Dai Liu and Linfeng Zhang and Guang Li and Kun Wang and Zheng Zhu and Zhiheng Ma and Joey Tianyi Zhou and Jiancheng Lv and Yaochu Jin and Peihao Wang and Kaipeng Zhang and Lingjuan Lyu and Yiran Huang and Zeynep Akata and Zhiwei Deng and Xindi Wu and George Cazenavette and Yuzhang Shang and Justin Cui and Jindong Gu and Qian Zheng and Hao Ye and Shuo Wang and Xiaobo Wang and Yan Yan and Angela Yao and Mike Zheng Shou and Tianlong Chen and Hakan Bilen and Baharan Mirzasoleiman and Manolis Kellis and Konstantinos N. Plataniotis and Zhangyang Wang and Bo Zhao and Yang You and Kai Wang },
  journal={arXiv preprint arXiv:2505.13300},
  year={ 2025 }
}