Fixed Anchors Are Not Enough: Dynamic Retrieval and Persistent Homology for Dataset Distillation

• URL: http://arxiv.org/abs/2602.24144v1
• Date: Fri, 27 Feb 2026 16:21:07 GMT
• Title: Fixed Anchors Are Not Enough: Dynamic Retrieval and Persistent Homology for Dataset Distillation
• Abstract summary: We introduce RETA -- a Retrieval and Topology Alignment framework for decoupled DD.<n>First, Dynamic Retrieval Connection (DRC) selects a real patch from a prebuilt pool.<n>Second, Persistent Topology Alignment (PTA) regularizes synthesis with persistent homology.
• Score: 18.560389397187794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decoupled dataset distillation (DD) compresses large corpora into a few synthetic images by matching a frozen teacher's statistics. However, current residual-matching pipelines rely on static real patches, creating a fit-complexity gap and a pull-to-anchor effect that reduce intra-class diversity and hurt generalization. To address these issues, we introduce RETA -- a Retrieval and Topology Alignment framework for decoupled DD. First, Dynamic Retrieval Connection (DRC) selects a real patch from a prebuilt pool by minimizing a fit-complexity score in teacher feature space; the chosen patch is injected via a residual connection to tighten feature fit while controlling injected complexity. Second, Persistent Topology Alignment (PTA) regularizes synthesis with persistent homology: we build a mutual k-NN feature graph, compute persistence images of components and loops, and penalize topology discrepancies between real and synthetic sets, mitigating pull-to-anchor effect. Across CIFAR-100, Tiny-ImageNet, ImageNet-1K, and multiple ImageNet subsets, RETA consistently outperforms various baselines under comparable time and memory, especially reaching 64.3% top-1 accuracy on ImageNet-1K with ResNet-18 at 50 images per class, +3.1% over the best prior.

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