Generative Dataset Distillation: Balancing Global Structure and Local Details

arXiv:2404.17732v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cs.ai cs.cv cs.lg dataset distillation generative global information paper reduce the information training training models type