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Elucidating the Design Space of Dataset Condensation
April 23, 2024, 4:42 a.m. | Shitong Shao, Zikai Zhou, Huanran Chen, Zhiqiang Shen
cs.LG updates on arXiv.org arxiv.org
Abstract: Dataset condensation, a concept within data-centric learning, efficiently transfers critical attributes from an original dataset to a synthetic version, maintaining both diversity and realism. This approach significantly improves model training efficiency and is adaptable across multiple application areas. Previous methods in dataset condensation have faced challenges: some incur high computational costs which limit scalability to larger datasets (e.g., MTT, DREAM, and TESLA), while others are restricted to less optimal design spaces, which could hinder potential …
abstract application arxiv challenges concept cs.ai cs.cv cs.lg data data-centric dataset design diversity efficiency multiple space synthetic training type
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