Dataset Condensation with Latent Quantile Matching

Wei Wei, Tom De Schepper, Kevin Mets

CVPR 2024 Workshop · first public 2024-06

paper ↗catalogued✓ abstract read

In one paragraph

Shows that matching only the mean of latent feature embeddings, as in standard distribution matching, lets very different distributions appear identical, and proposes Latent Quantile Matching, which instead matches the quantiles of the embedding distributions to minimize a goodness-of-fit statistic; matches or beats prior distribution-matching methods on image and graph-structured datasets and improves continual graph learning.

Where it sits

Distribution and feature matching (Surrogate matching)
Setting: Image classification
Setting: Graphs

Builds on

BibTeX (generated; prefer the venue's official entry)

@article{wei2024dataset,
  title   = {Dataset Condensation with Latent Quantile Matching},
  author  = {Wei Wei and Tom De Schepper and Kevin Mets},
  journal = {CVPR 2024 Workshop},
  year    = {2024}
}

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