{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:W67YVCIFIU4HX2U3TF4BQ3Y4UM","short_pith_number":"pith:W67YVCIF","schema_version":"1.0","canonical_sha256":"b7bf8a890545387bea9b9978186f1ca3023200a4c893cc27164bde7b046715cb","source":{"kind":"arxiv","id":"2303.10774","version":2},"attestation_state":"computed","paper":{"title":"Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Jayaraman J. Thiagarajan, Matthew L. Olson, Peer-Timo Bremer, Rushil Anirudh, Shusen Liu, Weng-Keen Wong","submitted_at":"2023-03-19T21:54:13Z","abstract_excerpt":"Generative Adversarial Networks (GANs) are notoriously difficult to train especially for complex distributions and with limited data. This has driven the need for tools to audit trained networks in human intelligible format, for example, to identify biases or ensure fairness. Existing GAN audit tools are restricted to coarse-grained, model-data comparisons based on summary statistics such as FID or recall. In this paper, we propose an alternative approach that compares a newly developed GAN against a prior baseline. To this end, we introduce Cross-GAN Auditing (xGA) that, given an established "},"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":"2303.10774","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-19T21:54:13Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"bceb8e03f34b6f7d0fbae83fc07367790671950937cfcebbab1bfe7d6ef582a7","abstract_canon_sha256":"9a527454b535d6f984fd89f5cf622a1d988f5a7600dad78e5901be1fbc850bcc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:06:15.581572Z","signature_b64":"gXbuk9SKRyvWUtD7ISqnTp2CoomXnHFxFblQdWZLoAymnhWzK3hgRHAE7KvuYsNBQrnyK9ft8dVaDyB54hSbAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7bf8a890545387bea9b9978186f1ca3023200a4c893cc27164bde7b046715cb","last_reissued_at":"2026-07-05T06:06:15.581138Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:06:15.581138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Jayaraman J. Thiagarajan, Matthew L. Olson, Peer-Timo Bremer, Rushil Anirudh, Shusen Liu, Weng-Keen Wong","submitted_at":"2023-03-19T21:54:13Z","abstract_excerpt":"Generative Adversarial Networks (GANs) are notoriously difficult to train especially for complex distributions and with limited data. This has driven the need for tools to audit trained networks in human intelligible format, for example, to identify biases or ensure fairness. Existing GAN audit tools are restricted to coarse-grained, model-data comparisons based on summary statistics such as FID or recall. In this paper, we propose an alternative approach that compares a newly developed GAN against a prior baseline. To this end, we introduce Cross-GAN Auditing (xGA) that, given an established "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.10774","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/2303.10774/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":"2303.10774","created_at":"2026-07-05T06:06:15.581206+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.10774v2","created_at":"2026-07-05T06:06:15.581206+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.10774","created_at":"2026-07-05T06:06:15.581206+00:00"},{"alias_kind":"pith_short_12","alias_value":"W67YVCIFIU4H","created_at":"2026-07-05T06:06:15.581206+00:00"},{"alias_kind":"pith_short_16","alias_value":"W67YVCIFIU4HX2U3","created_at":"2026-07-05T06:06:15.581206+00:00"},{"alias_kind":"pith_short_8","alias_value":"W67YVCIF","created_at":"2026-07-05T06:06:15.581206+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/W67YVCIFIU4HX2U3TF4BQ3Y4UM","json":"https://pith.science/pith/W67YVCIFIU4HX2U3TF4BQ3Y4UM.json","graph_json":"https://pith.science/api/pith-number/W67YVCIFIU4HX2U3TF4BQ3Y4UM/graph.json","events_json":"https://pith.science/api/pith-number/W67YVCIFIU4HX2U3TF4BQ3Y4UM/events.json","paper":"https://pith.science/paper/W67YVCIF"},"agent_actions":{"view_html":"https://pith.science/pith/W67YVCIFIU4HX2U3TF4BQ3Y4UM","download_json":"https://pith.science/pith/W67YVCIFIU4HX2U3TF4BQ3Y4UM.json","view_paper":"https://pith.science/paper/W67YVCIF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.10774&json=true","fetch_graph":"https://pith.science/api/pith-number/W67YVCIFIU4HX2U3TF4BQ3Y4UM/graph.json","fetch_events":"https://pith.science/api/pith-number/W67YVCIFIU4HX2U3TF4BQ3Y4UM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W67YVCIFIU4HX2U3TF4BQ3Y4UM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W67YVCIFIU4HX2U3TF4BQ3Y4UM/action/storage_attestation","attest_author":"https://pith.science/pith/W67YVCIFIU4HX2U3TF4BQ3Y4UM/action/author_attestation","sign_citation":"https://pith.science/pith/W67YVCIFIU4HX2U3TF4BQ3Y4UM/action/citation_signature","submit_replication":"https://pith.science/pith/W67YVCIFIU4HX2U3TF4BQ3Y4UM/action/replication_record"}},"created_at":"2026-07-05T06:06:15.581206+00:00","updated_at":"2026-07-05T06:06:15.581206+00:00"}