Method · Synthetic-set parameterization
SPEED
Sparse Parameterization for Epitomic Dataset Distillation
Xing Wei, Anjia Cao, Funing Yang, Zhiheng Ma
NeurIPS 2023 · first public 2023-01
In one paragraph
Applies dictionary learning and sparse coding to dataset distillation: Spatial-Agnostic Epitomic Tokens and Sparse Coding Matrices represent and select the most significant features, decoded by a Feature-Recurrent Network into hierarchical, high-compression synthetic images; reports state-of-the-art results on high-resolution benchmarks and downstream applications, compatible with a variety of matching objectives.
Where it sits
- Synthetic-set parameterization (Orthogonal design choices)
- Setting: Image classification
Design choices
| What is stored | sparse-dictionary |
BibTeX (generated; prefer the venue's official entry)
@article{wei2023sparse,
title = {Sparse Parameterization for Epitomic Dataset Distillation},
author = {Xing Wei and Anjia Cao and Funing Yang and Zhiheng Ma},
journal = {NeurIPS 2023},
year = {2023}
}Nearby in Synthetic-set parameterization
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