{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FPTTUQ4TCZSWCMCY6IIU4SRNPJ","short_pith_number":"pith:FPTTUQ4T","schema_version":"1.0","canonical_sha256":"2be73a43931665613058f2114e4a2d7a7028a2354d90180e62ec976e0467760d","source":{"kind":"arxiv","id":"2307.08950","version":1},"attestation_state":"computed","paper":{"title":"Deep Physics-Guided Unrolling Generalization for Compressed Sensing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Bin Chen, Jian Zhang, Jiechong Song, Jingfen Xie","submitted_at":"2023-07-18T03:37:10Z","abstract_excerpt":"By absorbing the merits of both the model- and data-driven methods, deep physics-engaged learning scheme achieves high-accuracy and interpretable image reconstruction. It has attracted growing attention and become the mainstream for inverse imaging tasks. Focusing on the image compressed sensing (CS) problem, we find the intrinsic defect of this emerging paradigm, widely implemented by deep algorithm-unrolled networks, in which more plain iterations involving real physics will bring enormous computation cost and long inference time, hindering their practical application. A novel deep $\\textbf{"},"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":"2307.08950","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-18T03:37:10Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"61adcdb65a30b2e056df34d65ce478ef75a5e71d069f5317ed19395d49c315ae","abstract_canon_sha256":"62d30e8bfb7337bcfff0e543e12cb877139732797cc7d3ac0fa1a51582d52a85"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:31:36.628442Z","signature_b64":"y6WCK6DtobEVp2B6W8OJkArQpu+ofnubdaZomSAMLgRGGenZ6tRgZ0bOZjd8p0dCmp9aqUvlRMuI7wtOMGGTAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2be73a43931665613058f2114e4a2d7a7028a2354d90180e62ec976e0467760d","last_reissued_at":"2026-07-05T06:31:36.627912Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:31:36.627912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Physics-Guided Unrolling Generalization for Compressed Sensing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Bin Chen, Jian Zhang, Jiechong Song, Jingfen Xie","submitted_at":"2023-07-18T03:37:10Z","abstract_excerpt":"By absorbing the merits of both the model- and data-driven methods, deep physics-engaged learning scheme achieves high-accuracy and interpretable image reconstruction. It has attracted growing attention and become the mainstream for inverse imaging tasks. Focusing on the image compressed sensing (CS) problem, we find the intrinsic defect of this emerging paradigm, widely implemented by deep algorithm-unrolled networks, in which more plain iterations involving real physics will bring enormous computation cost and long inference time, hindering their practical application. A novel deep $\\textbf{"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.08950","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/2307.08950/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":"2307.08950","created_at":"2026-07-05T06:31:36.627976+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.08950v1","created_at":"2026-07-05T06:31:36.627976+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.08950","created_at":"2026-07-05T06:31:36.627976+00:00"},{"alias_kind":"pith_short_12","alias_value":"FPTTUQ4TCZSW","created_at":"2026-07-05T06:31:36.627976+00:00"},{"alias_kind":"pith_short_16","alias_value":"FPTTUQ4TCZSWCMCY","created_at":"2026-07-05T06:31:36.627976+00:00"},{"alias_kind":"pith_short_8","alias_value":"FPTTUQ4T","created_at":"2026-07-05T06:31:36.627976+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/FPTTUQ4TCZSWCMCY6IIU4SRNPJ","json":"https://pith.science/pith/FPTTUQ4TCZSWCMCY6IIU4SRNPJ.json","graph_json":"https://pith.science/api/pith-number/FPTTUQ4TCZSWCMCY6IIU4SRNPJ/graph.json","events_json":"https://pith.science/api/pith-number/FPTTUQ4TCZSWCMCY6IIU4SRNPJ/events.json","paper":"https://pith.science/paper/FPTTUQ4T"},"agent_actions":{"view_html":"https://pith.science/pith/FPTTUQ4TCZSWCMCY6IIU4SRNPJ","download_json":"https://pith.science/pith/FPTTUQ4TCZSWCMCY6IIU4SRNPJ.json","view_paper":"https://pith.science/paper/FPTTUQ4T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.08950&json=true","fetch_graph":"https://pith.science/api/pith-number/FPTTUQ4TCZSWCMCY6IIU4SRNPJ/graph.json","fetch_events":"https://pith.science/api/pith-number/FPTTUQ4TCZSWCMCY6IIU4SRNPJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FPTTUQ4TCZSWCMCY6IIU4SRNPJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FPTTUQ4TCZSWCMCY6IIU4SRNPJ/action/storage_attestation","attest_author":"https://pith.science/pith/FPTTUQ4TCZSWCMCY6IIU4SRNPJ/action/author_attestation","sign_citation":"https://pith.science/pith/FPTTUQ4TCZSWCMCY6IIU4SRNPJ/action/citation_signature","submit_replication":"https://pith.science/pith/FPTTUQ4TCZSWCMCY6IIU4SRNPJ/action/replication_record"}},"created_at":"2026-07-05T06:31:36.627976+00:00","updated_at":"2026-07-05T06:31:36.627976+00:00"}