{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JRDVFRT47VAYT6XSBDQRDRJYVI","short_pith_number":"pith:JRDVFRT4","schema_version":"1.0","canonical_sha256":"4c4752c67cfd4189faf208e111c538aa166ce0e85aa3530679f9c9546276bf07","source":{"kind":"arxiv","id":"2412.05466","version":1},"attestation_state":"computed","paper":{"title":"Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Abdulrahman Kerim, Erickson R. Nascimento, Leandro Soriano Marcolino, Richard Jiang","submitted_at":"2024-12-06T23:36:36Z","abstract_excerpt":"Supervised machine learning methods require large-scale training datasets to perform well in practice. Synthetic data has been showing great progress recently and has been used as a complement to real data. However, there is yet a great urge to assess the usability of synthetically generated data. To this end, we propose a novel UCB-based training procedure combined with a dynamic usability metric. Our proposed metric integrates low-level and high-level information from synthetic images and their corresponding real and synthetic datasets, surpassing existing traditional metrics. By utilizing a"},"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":"2412.05466","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-06T23:36:36Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"0fbd2224bda6779e292cf7acaa8069244b75aadb531ece62cf93b4e1230647de","abstract_canon_sha256":"c09236f01c475895236057f24003ec028a5370d59d76b683fee02e3efa4b0f4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:03.722635Z","signature_b64":"KOuANmup8gfRxnjEjp06ptQ9ydLYAupFahoRg57/cgvTc52isqciDNVqsWwi3g80zsKugW1URKh6nKo6Jy6HDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c4752c67cfd4189faf208e111c538aa166ce0e85aa3530679f9c9546276bf07","last_reissued_at":"2026-07-05T09:46:03.722163Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:03.722163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Abdulrahman Kerim, Erickson R. Nascimento, Leandro Soriano Marcolino, Richard Jiang","submitted_at":"2024-12-06T23:36:36Z","abstract_excerpt":"Supervised machine learning methods require large-scale training datasets to perform well in practice. Synthetic data has been showing great progress recently and has been used as a complement to real data. However, there is yet a great urge to assess the usability of synthetically generated data. To this end, we propose a novel UCB-based training procedure combined with a dynamic usability metric. Our proposed metric integrates low-level and high-level information from synthetic images and their corresponding real and synthetic datasets, surpassing existing traditional metrics. By utilizing a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05466","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/2412.05466/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":"2412.05466","created_at":"2026-07-05T09:46:03.722222+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.05466v1","created_at":"2026-07-05T09:46:03.722222+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05466","created_at":"2026-07-05T09:46:03.722222+00:00"},{"alias_kind":"pith_short_12","alias_value":"JRDVFRT47VAY","created_at":"2026-07-05T09:46:03.722222+00:00"},{"alias_kind":"pith_short_16","alias_value":"JRDVFRT47VAYT6XS","created_at":"2026-07-05T09:46:03.722222+00:00"},{"alias_kind":"pith_short_8","alias_value":"JRDVFRT4","created_at":"2026-07-05T09:46:03.722222+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/JRDVFRT47VAYT6XSBDQRDRJYVI","json":"https://pith.science/pith/JRDVFRT47VAYT6XSBDQRDRJYVI.json","graph_json":"https://pith.science/api/pith-number/JRDVFRT47VAYT6XSBDQRDRJYVI/graph.json","events_json":"https://pith.science/api/pith-number/JRDVFRT47VAYT6XSBDQRDRJYVI/events.json","paper":"https://pith.science/paper/JRDVFRT4"},"agent_actions":{"view_html":"https://pith.science/pith/JRDVFRT47VAYT6XSBDQRDRJYVI","download_json":"https://pith.science/pith/JRDVFRT47VAYT6XSBDQRDRJYVI.json","view_paper":"https://pith.science/paper/JRDVFRT4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.05466&json=true","fetch_graph":"https://pith.science/api/pith-number/JRDVFRT47VAYT6XSBDQRDRJYVI/graph.json","fetch_events":"https://pith.science/api/pith-number/JRDVFRT47VAYT6XSBDQRDRJYVI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JRDVFRT47VAYT6XSBDQRDRJYVI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JRDVFRT47VAYT6XSBDQRDRJYVI/action/storage_attestation","attest_author":"https://pith.science/pith/JRDVFRT47VAYT6XSBDQRDRJYVI/action/author_attestation","sign_citation":"https://pith.science/pith/JRDVFRT47VAYT6XSBDQRDRJYVI/action/citation_signature","submit_replication":"https://pith.science/pith/JRDVFRT47VAYT6XSBDQRDRJYVI/action/replication_record"}},"created_at":"2026-07-05T09:46:03.722222+00:00","updated_at":"2026-07-05T09:46:03.722222+00:00"}