Method · Synthetic-set parameterization
Slimmable DC
Slimmable Dataset Condensation
Songhua Liu, Jingwen Ye, Runpeng Yu, Xinchao Wang
CVPR 2023 · first public 2023-01
In one paragraph
Introduces slimmable dataset condensation: a significance-aware parameterization whose components can be truncated to shrink an already-condensed dataset to a smaller storage budget without re-accessing the original data, addressing the inconsistency of matching networks over time and the underdetermined solution space that make naive successive compression fail; a theoretical bound shows discarding minor components is safe, and using them as initialization for further training gives fast convergence.
Where it sits
- Synthetic-set parameterization (Orthogonal design choices)
- Setting: Image classification
Design choices
| What is stored | other |
BibTeX (generated; prefer the venue's official entry)
@article{liu2023slimmable,
title = {Slimmable Dataset Condensation},
author = {Songhua Liu and Jingwen Ye and Runpeng Yu and Xinchao Wang},
journal = {CVPR 2023},
year = {2023}
}Nearby in Synthetic-set parameterization
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