mlsanityData to 14 Oct 2026

DiRe: Diversity-promoting Regularization for Dataset Condensation

arXiv 2512.13083 ↗First posted 15 Dec 2025cs.CV cs.LGcited by 1

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Abstract (arXiv)

In Dataset Condensation, the goal is to synthesize a small dataset that replicates the training utility of a large original dataset. Existing condensation methods synthesize datasets with significant redundancy, so there is a dire need to reduce redundancy and improve the diversity of the synthesized datasets. To tackle this, we propose an intuitive Diversity Regularizer (DiRe) composed of cosine similarity and Euclidean distance, which can be applied off-the-shelf to various state-of-the-art condensation methods. Through extensive experiments, we demonstrate that the addition of our regularizer improves state-of-the-art condensation methods on various benchmark datasets from CIFAR-10 to ImageNet-1K with respect to generalization and diversity metrics.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 49 of its 57 references that are in the map. First posted on arXiv 15 Dec 2025.

Builds on

49 of 57 references.

References · 49

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37 more
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  19. 2024Elucidating the Design Space of Dataset Condensationcited by 40
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  21. 2022Submodularity In Machine Learning and Artificial Intelligencecited by 38
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  27. 2024SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matchingcited by 22
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026X-AVDD: Cross-Attentive Audio-Visual Dataset Distillationcited by 0