{"paper":{"title":"A Discrepancy-Based Perspective on Dataset Condensation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Raghavendra Selvan, Tong Chen","submitted_at":"2025-09-12T16:00:49Z","abstract_excerpt":"Given a dataset of finitely many elements $\\mathcal{T} = \\{\\mathbf{x}_i\\}_{i = 1}^N$, the goal of dataset condensation (DC) is to construct a synthetic dataset $\\mathcal{S} = \\{\\tilde{\\mathbf{x}}_j\\}_{j = 1}^M$ which is significantly smaller ($M \\ll N$) such that a model trained from scratch on $\\mathcal{S}$ achieves comparable or even superior generalization performance to a model trained on $\\mathcal{T}$. Recent advances in DC reveal a close connection to the problem of approximating the data distribution represented by $\\mathcal{T}$ with a reduced set of points. In this work, we present a u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.10367","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.10367/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}