{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7SIQMQI2T5PC76BOY2DOLF2QVY","short_pith_number":"pith:7SIQMQI2","schema_version":"1.0","canonical_sha256":"fc9106411a9f5e2ff82ec686e59750ae0ec5610d5abb60bfd03b4f4965b08d52","source":{"kind":"arxiv","id":"2305.01649","version":2},"attestation_state":"computed","paper":{"title":"Generalizing Dataset Distillation via Deep Generative Prior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexei A. Efros, Antonio Torralba, George Cazenavette, Jun-Yan Zhu, Tongzhou Wang","submitted_at":"2023-05-02T17:59:31Z","abstract_excerpt":"Dataset Distillation aims to distill an entire dataset's knowledge into a few synthetic images. The idea is to synthesize a small number of synthetic data points that, when given to a learning algorithm as training data, result in a model approximating one trained on the original data. Despite recent progress in the field, existing dataset distillation methods fail to generalize to new architectures and scale to high-resolution datasets. To overcome the above issues, we propose to use the learned prior from pre-trained deep generative models to synthesize the distilled data. To achieve this, w"},"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":"2305.01649","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-02T17:59:31Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"d8d9e8c4f9f430152fbe69ade40d7f64c33f2fdc3461f23d56afaf0f3e39d176","abstract_canon_sha256":"3f778dc3782909019607921831d92f8870b77fec6e6069ef33dbcaedf9cdd095"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:06:59.199533Z","signature_b64":"HqSMv9zFKQ1n3hKABxEZ/UfFMvkvmQLNSvM7Uvvz3TMCXtnVsiY2KJBa3KZyLALHarjvSixipqNQhSgFPVDjBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc9106411a9f5e2ff82ec686e59750ae0ec5610d5abb60bfd03b4f4965b08d52","last_reissued_at":"2026-07-05T06:06:59.199114Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:06:59.199114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalizing Dataset Distillation via Deep Generative Prior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexei A. Efros, Antonio Torralba, George Cazenavette, Jun-Yan Zhu, Tongzhou Wang","submitted_at":"2023-05-02T17:59:31Z","abstract_excerpt":"Dataset Distillation aims to distill an entire dataset's knowledge into a few synthetic images. The idea is to synthesize a small number of synthetic data points that, when given to a learning algorithm as training data, result in a model approximating one trained on the original data. Despite recent progress in the field, existing dataset distillation methods fail to generalize to new architectures and scale to high-resolution datasets. To overcome the above issues, we propose to use the learned prior from pre-trained deep generative models to synthesize the distilled data. To achieve this, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.01649","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/2305.01649/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":"2305.01649","created_at":"2026-07-05T06:06:59.199170+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.01649v2","created_at":"2026-07-05T06:06:59.199170+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.01649","created_at":"2026-07-05T06:06:59.199170+00:00"},{"alias_kind":"pith_short_12","alias_value":"7SIQMQI2T5PC","created_at":"2026-07-05T06:06:59.199170+00:00"},{"alias_kind":"pith_short_16","alias_value":"7SIQMQI2T5PC76BO","created_at":"2026-07-05T06:06:59.199170+00:00"},{"alias_kind":"pith_short_8","alias_value":"7SIQMQI2","created_at":"2026-07-05T06:06:59.199170+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.20694","citing_title":"Temporal Saliency-Guided Distillation: A Scalable Framework for Distilling Video Datasets","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7SIQMQI2T5PC76BOY2DOLF2QVY","json":"https://pith.science/pith/7SIQMQI2T5PC76BOY2DOLF2QVY.json","graph_json":"https://pith.science/api/pith-number/7SIQMQI2T5PC76BOY2DOLF2QVY/graph.json","events_json":"https://pith.science/api/pith-number/7SIQMQI2T5PC76BOY2DOLF2QVY/events.json","paper":"https://pith.science/paper/7SIQMQI2"},"agent_actions":{"view_html":"https://pith.science/pith/7SIQMQI2T5PC76BOY2DOLF2QVY","download_json":"https://pith.science/pith/7SIQMQI2T5PC76BOY2DOLF2QVY.json","view_paper":"https://pith.science/paper/7SIQMQI2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.01649&json=true","fetch_graph":"https://pith.science/api/pith-number/7SIQMQI2T5PC76BOY2DOLF2QVY/graph.json","fetch_events":"https://pith.science/api/pith-number/7SIQMQI2T5PC76BOY2DOLF2QVY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7SIQMQI2T5PC76BOY2DOLF2QVY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7SIQMQI2T5PC76BOY2DOLF2QVY/action/storage_attestation","attest_author":"https://pith.science/pith/7SIQMQI2T5PC76BOY2DOLF2QVY/action/author_attestation","sign_citation":"https://pith.science/pith/7SIQMQI2T5PC76BOY2DOLF2QVY/action/citation_signature","submit_replication":"https://pith.science/pith/7SIQMQI2T5PC76BOY2DOLF2QVY/action/replication_record"}},"created_at":"2026-07-05T06:06:59.199170+00:00","updated_at":"2026-07-05T06:06:59.199170+00:00"}