{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RFFUFINH3NZO7GCC2D3VEZVRHZ","short_pith_number":"pith:RFFUFINH","schema_version":"1.0","canonical_sha256":"894b42a1a7db72ef9842d0f75266b13e51b08a6bda92a638a82c2c68493640e4","source":{"kind":"arxiv","id":"2102.08921","version":2},"attestation_state":"computed","paper":{"title":"How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ahmed M. Alaa, Boris van Breugel, Evgeny Saveliev, Mihaela van der Schaar","submitted_at":"2021-02-17T18:25:30Z","abstract_excerpt":"Devising domain- and model-agnostic evaluation metrics for generative models is an important and as yet unresolved problem. Most existing metrics, which were tailored solely to the image synthesis setup, exhibit a limited capacity for diagnosing the different modes of failure of generative models across broader application domains. In this paper, we introduce a 3-dimensional evaluation metric, ($\\alpha$-Precision, $\\beta$-Recall, Authenticity), that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion. Our metric unifies stat"},"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":"2102.08921","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-02-17T18:25:30Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b3d9bb33c0183cf930390ad95f7393bf8b8eef8779a4c5c248d0a115f0cd28e4","abstract_canon_sha256":"ca2e00d068a5df6ddb397cbbed54f107db0fc60c924d4de8357dc9ff2e50f032"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:39:59.662454Z","signature_b64":"0sPa6dNynVckTf/cxk3vuk60w4ynUysRaoRVuhNRDo5x/LKcDOhSegfJWf/7m7Tcl8lWNMec3AUA8r/m7dgGCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"894b42a1a7db72ef9842d0f75266b13e51b08a6bda92a638a82c2c68493640e4","last_reissued_at":"2026-07-05T04:39:59.662012Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:39:59.662012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ahmed M. Alaa, Boris van Breugel, Evgeny Saveliev, Mihaela van der Schaar","submitted_at":"2021-02-17T18:25:30Z","abstract_excerpt":"Devising domain- and model-agnostic evaluation metrics for generative models is an important and as yet unresolved problem. Most existing metrics, which were tailored solely to the image synthesis setup, exhibit a limited capacity for diagnosing the different modes of failure of generative models across broader application domains. In this paper, we introduce a 3-dimensional evaluation metric, ($\\alpha$-Precision, $\\beta$-Recall, Authenticity), that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion. Our metric unifies stat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.08921","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/2102.08921/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":"2102.08921","created_at":"2026-07-05T04:39:59.662069+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.08921v2","created_at":"2026-07-05T04:39:59.662069+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.08921","created_at":"2026-07-05T04:39:59.662069+00:00"},{"alias_kind":"pith_short_12","alias_value":"RFFUFINH3NZO","created_at":"2026-07-05T04:39:59.662069+00:00"},{"alias_kind":"pith_short_16","alias_value":"RFFUFINH3NZO7GCC","created_at":"2026-07-05T04:39:59.662069+00:00"},{"alias_kind":"pith_short_8","alias_value":"RFFUFINH","created_at":"2026-07-05T04:39:59.662069+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/RFFUFINH3NZO7GCC2D3VEZVRHZ","json":"https://pith.science/pith/RFFUFINH3NZO7GCC2D3VEZVRHZ.json","graph_json":"https://pith.science/api/pith-number/RFFUFINH3NZO7GCC2D3VEZVRHZ/graph.json","events_json":"https://pith.science/api/pith-number/RFFUFINH3NZO7GCC2D3VEZVRHZ/events.json","paper":"https://pith.science/paper/RFFUFINH"},"agent_actions":{"view_html":"https://pith.science/pith/RFFUFINH3NZO7GCC2D3VEZVRHZ","download_json":"https://pith.science/pith/RFFUFINH3NZO7GCC2D3VEZVRHZ.json","view_paper":"https://pith.science/paper/RFFUFINH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.08921&json=true","fetch_graph":"https://pith.science/api/pith-number/RFFUFINH3NZO7GCC2D3VEZVRHZ/graph.json","fetch_events":"https://pith.science/api/pith-number/RFFUFINH3NZO7GCC2D3VEZVRHZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RFFUFINH3NZO7GCC2D3VEZVRHZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RFFUFINH3NZO7GCC2D3VEZVRHZ/action/storage_attestation","attest_author":"https://pith.science/pith/RFFUFINH3NZO7GCC2D3VEZVRHZ/action/author_attestation","sign_citation":"https://pith.science/pith/RFFUFINH3NZO7GCC2D3VEZVRHZ/action/citation_signature","submit_replication":"https://pith.science/pith/RFFUFINH3NZO7GCC2D3VEZVRHZ/action/replication_record"}},"created_at":"2026-07-05T04:39:59.662069+00:00","updated_at":"2026-07-05T04:39:59.662069+00:00"}