Method · Synthetic-set parameterization
An Efficient Dataset Condensation Plugin and Its Application to Continual Learning
Enneng Yang, Li Shen, Zhenyi Wang, Tongliang Liu, Guibing Guo
NeurIPS 2023 · first public 2023-01-01
In one paragraph
Observes that dataset-condensation matching objectives operate in high-dimensional pixel space even though natural images are locally connected and have lower intrinsic dimension, and proposes a plugin that instead condenses images into two low-rank matrices, matching raw and synthetic data in this low-dimensional manifold for higher condensation efficiency. Demonstrated as a continual-learning replay buffer, presented at NeurIPS 2023.
Where it sits
- Synthetic-set parameterization (Orthogonal design choices)
- Setting: Image classification
Design choices
| What is stored | factorized-bases |
BibTeX (generated; prefer the venue's official entry)
@article{yang2023efficient,
title = {An Efficient Dataset Condensation Plugin and Its Application to Continual Learning},
author = {Enneng Yang and Li Shen and Zhenyi Wang and Tongliang Liu and Guibing Guo},
journal = {NeurIPS 2023},
year = {2023}
}Nearby in Synthetic-set parameterization
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