{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EMXLVV3GOG5WTLLNWVRYU6BL27","short_pith_number":"pith:EMXLVV3G","schema_version":"1.0","canonical_sha256":"232ebad76671bb69ad6db5638a782bd7d5618ef9e2c18814c2ee279016ed96e5","source":{"kind":"arxiv","id":"2411.19946","version":2},"attestation_state":"computed","paper":{"title":"DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Ammar Sherif, Shitong Shao, Zeyuan Yin, Zhiqiang Shen","submitted_at":"2024-11-29T18:59:46Z","abstract_excerpt":"Recent advances in dataset distillation have led to solutions in two main directions. The conventional batch-to-batch matching mechanism is ideal for small-scale datasets and includes bi-level optimization methods on models and syntheses, such as FRePo, RCIG, and RaT-BPTT, as well as other methods like distribution matching, gradient matching, and weight trajectory matching. Conversely, batch-to-global matching typifies decoupled methods, which are particularly advantageous for large-scale datasets. This approach has garnered substantial interest within the community, as seen in SRe$^2$L, G-VB"},"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":"2411.19946","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-29T18:59:46Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"d75890a7bda0e4d3aea4f93ab93b23fc16c1f3874e4529acb79cf9d4ee9c7eec","abstract_canon_sha256":"d2b04635e9ff5e7ee622337bf5ad694d4e010c2768e331dc5d823b71f98c5ce2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:38.397517Z","signature_b64":"lHtQB1lBvuqTnB1X86CcIbKQ3CzRVAAc3e7k9/sQBEp+mGB9SLU4cfqCEei1XZFn2U2qH8TLDXFi6WuybnfFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"232ebad76671bb69ad6db5638a782bd7d5618ef9e2c18814c2ee279016ed96e5","last_reissued_at":"2026-07-05T11:17:38.397067Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:38.397067Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Ammar Sherif, Shitong Shao, Zeyuan Yin, Zhiqiang Shen","submitted_at":"2024-11-29T18:59:46Z","abstract_excerpt":"Recent advances in dataset distillation have led to solutions in two main directions. The conventional batch-to-batch matching mechanism is ideal for small-scale datasets and includes bi-level optimization methods on models and syntheses, such as FRePo, RCIG, and RaT-BPTT, as well as other methods like distribution matching, gradient matching, and weight trajectory matching. Conversely, batch-to-global matching typifies decoupled methods, which are particularly advantageous for large-scale datasets. This approach has garnered substantial interest within the community, as seen in SRe$^2$L, G-VB"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.19946","kind":"arxiv","version":2},"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/2411.19946/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":"2411.19946","created_at":"2026-07-05T11:17:38.397123+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.19946v2","created_at":"2026-07-05T11:17:38.397123+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.19946","created_at":"2026-07-05T11:17:38.397123+00:00"},{"alias_kind":"pith_short_12","alias_value":"EMXLVV3GOG5W","created_at":"2026-07-05T11:17:38.397123+00:00"},{"alias_kind":"pith_short_16","alias_value":"EMXLVV3GOG5WTLLN","created_at":"2026-07-05T11:17:38.397123+00:00"},{"alias_kind":"pith_short_8","alias_value":"EMXLVV3G","created_at":"2026-07-05T11:17:38.397123+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/EMXLVV3GOG5WTLLNWVRYU6BL27","json":"https://pith.science/pith/EMXLVV3GOG5WTLLNWVRYU6BL27.json","graph_json":"https://pith.science/api/pith-number/EMXLVV3GOG5WTLLNWVRYU6BL27/graph.json","events_json":"https://pith.science/api/pith-number/EMXLVV3GOG5WTLLNWVRYU6BL27/events.json","paper":"https://pith.science/paper/EMXLVV3G"},"agent_actions":{"view_html":"https://pith.science/pith/EMXLVV3GOG5WTLLNWVRYU6BL27","download_json":"https://pith.science/pith/EMXLVV3GOG5WTLLNWVRYU6BL27.json","view_paper":"https://pith.science/paper/EMXLVV3G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.19946&json=true","fetch_graph":"https://pith.science/api/pith-number/EMXLVV3GOG5WTLLNWVRYU6BL27/graph.json","fetch_events":"https://pith.science/api/pith-number/EMXLVV3GOG5WTLLNWVRYU6BL27/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EMXLVV3GOG5WTLLNWVRYU6BL27/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EMXLVV3GOG5WTLLNWVRYU6BL27/action/storage_attestation","attest_author":"https://pith.science/pith/EMXLVV3GOG5WTLLNWVRYU6BL27/action/author_attestation","sign_citation":"https://pith.science/pith/EMXLVV3GOG5WTLLNWVRYU6BL27/action/citation_signature","submit_replication":"https://pith.science/pith/EMXLVV3GOG5WTLLNWVRYU6BL27/action/replication_record"}},"created_at":"2026-07-05T11:17:38.397123+00:00","updated_at":"2026-07-05T11:17:38.397123+00:00"}