Dataset distillation (DD) condenses a large original dataset into a small distilled dataset with high training utility. Decoupled statistical matching methods substantially reduce distillation time and memory overhead while achieving strong performance. However, they typically supervise all distilled samples using runn...
Hong-Xu Ma, Guang Li, Shi-Jie Wang et al.· 0 citations
Medical image analysis often relies on large annotated datasets, whose collection, storage, and sharing are costly and constrained by privacy concerns. Dataset distillation offers a potential solution by constructing compact synthetic datasets that retain task-relevant information from the original data. However, gener...
Shuai-Ling Du, Guang Li, Ren Togo et al.· Bioengineering· 0 citations
DeCO uses attention rollout from a pretrained TransFG teacher to identify informative patches, applies spatial diversification to reduce redundant coverage, and organizes the resulting regions into class-wise evidence banks and consistently outperforms representative coreset and dataset-distillation baselines under dif...
Chuixuan Fan, Guang Li, Shi-Jie Wang et al.· 0 citations
This work proposes a saliency-driven distillation framework that constructs class-discriminative latent prototypes to enhance representativeness and generalization and demonstrates consistent performance improvements over strong baselines.
Yawen Zou, Wenqi Cai, Guang Li et al.· arXiv.org· 0 citations
Self-supervised representation-guided generative dataset distillation (SRG) is proposed, a framework that translates the SSL geometry into diffusion guidance and consistently outperforms the evaluated generative baselines across multiple datasets and IPC settings.
Mingzhuo Li, Guang Li, Linfeng Ye et al.· 0 citations
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