{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Z5LSXRO2HBALMHQDGDXROIN5QB","short_pith_number":"pith:Z5LSXRO2","schema_version":"1.0","canonical_sha256":"cf572bc5da3840b61e0330ef1721bd80481d41c4d758325ad9dd00e220f4880c","source":{"kind":"arxiv","id":"2407.15138","version":1},"attestation_state":"computed","paper":{"title":"D$^4$M: Dataset Distillation via Disentangled Diffusion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Tang, Duo Su, Junjie Hou, Weizhi Gao, Yingjie Tian","submitted_at":"2024-07-21T12:16:20Z","abstract_excerpt":"Dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2407.15138","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-21T12:16:20Z","cross_cats_sorted":[],"title_canon_sha256":"ec5f39419361503ac94fcd339e352d77dae6c348db027974ecb0e89647d2ef9d","abstract_canon_sha256":"d02fc87a0cbdb344b59048fafb0a3fb4fd51a762bb8da145ef9b34146c3fcd06"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:46:39.366365Z","signature_b64":"vf9tGPBQ0oIctECyj7yyojx88J5BgGsSCuxmslFg3xAfEt8WIdBgRcR6wyONNnPaO3D7/rzoOSnP5aJsjOhmAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf572bc5da3840b61e0330ef1721bd80481d41c4d758325ad9dd00e220f4880c","last_reissued_at":"2026-07-05T08:46:39.365954Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:46:39.365954Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"D$^4$M: Dataset Distillation via Disentangled Diffusion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Tang, Duo Su, Junjie Hou, Weizhi Gao, Yingjie Tian","submitted_at":"2024-07-21T12:16:20Z","abstract_excerpt":"Dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15138","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/2407.15138/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2407.15138","created_at":"2026-07-05T08:46:39.366024+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.15138v1","created_at":"2026-07-05T08:46:39.366024+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.15138","created_at":"2026-07-05T08:46:39.366024+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z5LSXRO2HBAL","created_at":"2026-07-05T08:46:39.366024+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z5LSXRO2HBALMHQD","created_at":"2026-07-05T08:46:39.366024+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z5LSXRO2","created_at":"2026-07-05T08:46:39.366024+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z5LSXRO2HBALMHQDGDXROIN5QB","json":"https://pith.science/pith/Z5LSXRO2HBALMHQDGDXROIN5QB.json","graph_json":"https://pith.science/api/pith-number/Z5LSXRO2HBALMHQDGDXROIN5QB/graph.json","events_json":"https://pith.science/api/pith-number/Z5LSXRO2HBALMHQDGDXROIN5QB/events.json","paper":"https://pith.science/paper/Z5LSXRO2"},"agent_actions":{"view_html":"https://pith.science/pith/Z5LSXRO2HBALMHQDGDXROIN5QB","download_json":"https://pith.science/pith/Z5LSXRO2HBALMHQDGDXROIN5QB.json","view_paper":"https://pith.science/paper/Z5LSXRO2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.15138&json=true","fetch_graph":"https://pith.science/api/pith-number/Z5LSXRO2HBALMHQDGDXROIN5QB/graph.json","fetch_events":"https://pith.science/api/pith-number/Z5LSXRO2HBALMHQDGDXROIN5QB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z5LSXRO2HBALMHQDGDXROIN5QB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z5LSXRO2HBALMHQDGDXROIN5QB/action/storage_attestation","attest_author":"https://pith.science/pith/Z5LSXRO2HBALMHQDGDXROIN5QB/action/author_attestation","sign_citation":"https://pith.science/pith/Z5LSXRO2HBALMHQDGDXROIN5QB/action/citation_signature","submit_replication":"https://pith.science/pith/Z5LSXRO2HBALMHQDGDXROIN5QB/action/replication_record"}},"created_at":"2026-07-05T08:46:39.366024+00:00","updated_at":"2026-07-05T08:46:39.366024+00:00"}