Generative Dataset Distillation: Balancing Global Structure and Local Details
<|reference_start|>Generative Dataset Distillation: Balancing Global Structure and Local Details: In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a generative model. Dataset distillation has been proposed to reduce the size of the required dataset when training models. The conventional dataset distillation methods face the problem of long redeployment time and poor cross-architecture performance. Moreover, previous methods focused too much on the high-level semantic attributes between the synthetic dataset and the original dataset while ignoring the local features such as texture and shape. Based on the above understanding, we propose a new method for distilling the original image dataset into a generative model. Our method involves using a conditional generative adversarial network to generate the distilled dataset. Subsequently, we ensure balancing global structure and local details in the distillation process, continuously optimizing the generator for more information-dense dataset generation.<|reference_end|>
@article{li2024generative,
    title={Generative Dataset Distillation: Balancing Global Structure and Local
  Details},
    author={Longzhen Li, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki
  Haseyama},
    journal={arXiv preprint arXiv:2404.17732},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17732},
    primaryClass={cs.CV cs.AI cs.LG}
}