{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MD7TYNVM56KTCRRTCVNF6GGL6D","short_pith_number":"pith:MD7TYNVM","schema_version":"1.0","canonical_sha256":"60ff3c36acef95314633155a5f18cbf0e5a5904532d68d2d2352431d69ac1eef","source":{"kind":"arxiv","id":"2502.01338","version":1},"attestation_state":"computed","paper":{"title":"PtyGenography: using generative models for regularization of the phase retrieval problem","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.FA","math.IT","math.OC"],"primary_cat":"stat.ML","authors_text":"Allard Mosk, Palina Salanevich, Selin Aslan, Tristan van Leeuwen","submitted_at":"2025-02-03T13:26:55Z","abstract_excerpt":"In phase retrieval and similar inverse problems, the stability of solutions across different noise levels is crucial for applications. One approach to promote it is using signal priors in a form of a generative model as a regularization, at the expense of introducing a bias in the reconstruction. In this paper, we explore and compare the reconstruction properties of classical and generative inverse problem formulations. We propose a new unified reconstruction approach that mitigates overfitting to the generative model for varying noise levels."},"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":"2502.01338","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-02-03T13:26:55Z","cross_cats_sorted":["cs.IT","cs.LG","math.FA","math.IT","math.OC"],"title_canon_sha256":"3b77f4cd15bd752257b71cfe9b822c8115fe1ea6b59d0bc4a71d1c388a67b2bf","abstract_canon_sha256":"a61ecf69bb031c99c24d30bc1c1a1aaa80b5f7238235403f47d1e6eaa7151467"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:45.904051Z","signature_b64":"EY+5wrEg7AAGCH1HckwwFRMIiYqyh/F9OS776F8//PiD2wCgsHqKq6bqASrLk0CjxnuxFFO5abeDGVFyJawWCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60ff3c36acef95314633155a5f18cbf0e5a5904532d68d2d2352431d69ac1eef","last_reissued_at":"2026-07-05T10:08:45.903646Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:45.903646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PtyGenography: using generative models for regularization of the phase retrieval problem","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.FA","math.IT","math.OC"],"primary_cat":"stat.ML","authors_text":"Allard Mosk, Palina Salanevich, Selin Aslan, Tristan van Leeuwen","submitted_at":"2025-02-03T13:26:55Z","abstract_excerpt":"In phase retrieval and similar inverse problems, the stability of solutions across different noise levels is crucial for applications. One approach to promote it is using signal priors in a form of a generative model as a regularization, at the expense of introducing a bias in the reconstruction. In this paper, we explore and compare the reconstruction properties of classical and generative inverse problem formulations. We propose a new unified reconstruction approach that mitigates overfitting to the generative model for varying noise levels."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01338","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/2502.01338/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":"2502.01338","created_at":"2026-07-05T10:08:45.903701+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01338v1","created_at":"2026-07-05T10:08:45.903701+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01338","created_at":"2026-07-05T10:08:45.903701+00:00"},{"alias_kind":"pith_short_12","alias_value":"MD7TYNVM56KT","created_at":"2026-07-05T10:08:45.903701+00:00"},{"alias_kind":"pith_short_16","alias_value":"MD7TYNVM56KTCRRT","created_at":"2026-07-05T10:08:45.903701+00:00"},{"alias_kind":"pith_short_8","alias_value":"MD7TYNVM","created_at":"2026-07-05T10:08:45.903701+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.15351","citing_title":"Phasebook: A Survey of Selected Open Problems in Phase Retrieval","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MD7TYNVM56KTCRRTCVNF6GGL6D","json":"https://pith.science/pith/MD7TYNVM56KTCRRTCVNF6GGL6D.json","graph_json":"https://pith.science/api/pith-number/MD7TYNVM56KTCRRTCVNF6GGL6D/graph.json","events_json":"https://pith.science/api/pith-number/MD7TYNVM56KTCRRTCVNF6GGL6D/events.json","paper":"https://pith.science/paper/MD7TYNVM"},"agent_actions":{"view_html":"https://pith.science/pith/MD7TYNVM56KTCRRTCVNF6GGL6D","download_json":"https://pith.science/pith/MD7TYNVM56KTCRRTCVNF6GGL6D.json","view_paper":"https://pith.science/paper/MD7TYNVM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01338&json=true","fetch_graph":"https://pith.science/api/pith-number/MD7TYNVM56KTCRRTCVNF6GGL6D/graph.json","fetch_events":"https://pith.science/api/pith-number/MD7TYNVM56KTCRRTCVNF6GGL6D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MD7TYNVM56KTCRRTCVNF6GGL6D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MD7TYNVM56KTCRRTCVNF6GGL6D/action/storage_attestation","attest_author":"https://pith.science/pith/MD7TYNVM56KTCRRTCVNF6GGL6D/action/author_attestation","sign_citation":"https://pith.science/pith/MD7TYNVM56KTCRRTCVNF6GGL6D/action/citation_signature","submit_replication":"https://pith.science/pith/MD7TYNVM56KTCRRTCVNF6GGL6D/action/replication_record"}},"created_at":"2026-07-05T10:08:45.903701+00:00","updated_at":"2026-07-05T10:08:45.903701+00:00"}