{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:GQXSEDEJ3HT6MKCSYPPUDWWP2U","short_pith_number":"pith:GQXSEDEJ","schema_version":"1.0","canonical_sha256":"342f220c89d9e7e62852c3df41dacfd5179536bb70ead1c1af7395b206249b88","source":{"kind":"arxiv","id":"2210.16774","version":1},"attestation_state":"computed","paper":{"title":"Dataset Distillation via Factorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jingwen Ye, Kai Wang, Songhua Liu, Xinchao Wang, Xingyi Yang","submitted_at":"2022-10-30T08:36:19Z","abstract_excerpt":"In this paper, we study \\xw{dataset distillation (DD)}, from a novel perspective and introduce a \\emph{dataset factorization} approach, termed \\emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline. Unlike conventional DD approaches that aim to produce distilled and representative samples, \\emph{HaBa} explores decomposing a dataset into two components: data \\emph{Ha}llucination networks and \\emph{Ba}ses, where the latter is fed into the former to reconstruct image samples. The flexible combinations between bases and hallucination networks, therefore, equip the dist"},"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":"2210.16774","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-30T08:36:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e33ca5fbe2f302e5c487f679f197dab660d969bf2032fec911608d64dc3b9cdf","abstract_canon_sha256":"e83f3833510f0b82057aaa8d78959e75f013f5bcc70b18b390a2e38b1ea3ce60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:11:50.521246Z","signature_b64":"HchOz4b7XnAxXSHqSni74BG1oLekChsJfG8xhn56zz0wqShJp1ACvK0NHtBW25NQA+zz3ADF6ahICDLwbgXDCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"342f220c89d9e7e62852c3df41dacfd5179536bb70ead1c1af7395b206249b88","last_reissued_at":"2026-07-05T05:11:50.520840Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:11:50.520840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dataset Distillation via Factorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jingwen Ye, Kai Wang, Songhua Liu, Xinchao Wang, Xingyi Yang","submitted_at":"2022-10-30T08:36:19Z","abstract_excerpt":"In this paper, we study \\xw{dataset distillation (DD)}, from a novel perspective and introduce a \\emph{dataset factorization} approach, termed \\emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline. Unlike conventional DD approaches that aim to produce distilled and representative samples, \\emph{HaBa} explores decomposing a dataset into two components: data \\emph{Ha}llucination networks and \\emph{Ba}ses, where the latter is fed into the former to reconstruct image samples. The flexible combinations between bases and hallucination networks, therefore, equip the dist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.16774","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/2210.16774/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":"2210.16774","created_at":"2026-07-05T05:11:50.520898+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.16774v1","created_at":"2026-07-05T05:11:50.520898+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.16774","created_at":"2026-07-05T05:11:50.520898+00:00"},{"alias_kind":"pith_short_12","alias_value":"GQXSEDEJ3HT6","created_at":"2026-07-05T05:11:50.520898+00:00"},{"alias_kind":"pith_short_16","alias_value":"GQXSEDEJ3HT6MKCS","created_at":"2026-07-05T05:11:50.520898+00:00"},{"alias_kind":"pith_short_8","alias_value":"GQXSEDEJ","created_at":"2026-07-05T05:11:50.520898+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/GQXSEDEJ3HT6MKCSYPPUDWWP2U","json":"https://pith.science/pith/GQXSEDEJ3HT6MKCSYPPUDWWP2U.json","graph_json":"https://pith.science/api/pith-number/GQXSEDEJ3HT6MKCSYPPUDWWP2U/graph.json","events_json":"https://pith.science/api/pith-number/GQXSEDEJ3HT6MKCSYPPUDWWP2U/events.json","paper":"https://pith.science/paper/GQXSEDEJ"},"agent_actions":{"view_html":"https://pith.science/pith/GQXSEDEJ3HT6MKCSYPPUDWWP2U","download_json":"https://pith.science/pith/GQXSEDEJ3HT6MKCSYPPUDWWP2U.json","view_paper":"https://pith.science/paper/GQXSEDEJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.16774&json=true","fetch_graph":"https://pith.science/api/pith-number/GQXSEDEJ3HT6MKCSYPPUDWWP2U/graph.json","fetch_events":"https://pith.science/api/pith-number/GQXSEDEJ3HT6MKCSYPPUDWWP2U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GQXSEDEJ3HT6MKCSYPPUDWWP2U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GQXSEDEJ3HT6MKCSYPPUDWWP2U/action/storage_attestation","attest_author":"https://pith.science/pith/GQXSEDEJ3HT6MKCSYPPUDWWP2U/action/author_attestation","sign_citation":"https://pith.science/pith/GQXSEDEJ3HT6MKCSYPPUDWWP2U/action/citation_signature","submit_replication":"https://pith.science/pith/GQXSEDEJ3HT6MKCSYPPUDWWP2U/action/replication_record"}},"created_at":"2026-07-05T05:11:50.520898+00:00","updated_at":"2026-07-05T05:11:50.520898+00:00"}