{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PNXC6UJEJV5SL7TYI3EWXKPQQK","short_pith_number":"pith:PNXC6UJE","schema_version":"1.0","canonical_sha256":"7b6e2f51244d7b25fe7846c96ba9f082880fcfb59df9cd79781f432186f5c5d4","source":{"kind":"arxiv","id":"2407.01027","version":1},"attestation_state":"computed","paper":{"title":"Blind Inversion using Latent Diffusion Priors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"He Sun, Siyi Chen, Weimin Bai, Wenzheng Chen","submitted_at":"2024-07-01T07:23:28Z","abstract_excerpt":"Diffusion models have emerged as powerful tools for solving inverse problems due to their exceptional ability to model complex prior distributions. However, existing methods predominantly assume known forward operators (i.e., non-blind), limiting their applicability in practical settings where acquiring such operators is costly. Additionally, many current approaches rely on pixel-space diffusion models, leaving the potential of more powerful latent diffusion models (LDMs) underexplored. In this paper, we introduce LatentDEM, an innovative technique that addresses more challenging blind inverse"},"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":"2407.01027","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-01T07:23:28Z","cross_cats_sorted":[],"title_canon_sha256":"3f17d2a9158c018efb9ca8f4834a969e9e1f37ceedc74c3c256bf106fa1530d4","abstract_canon_sha256":"be539f677a9a85ec868820dbf23d43c017e86d233890a5a5bfa817b00ab6d286"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:41.870308Z","signature_b64":"Wa4e2ggE6nhg7QF71yg6ep/nzIHa/l2cxJyAbi+ApdVK2xXFrQJmhf4Tx00i0YoGrPyt8GW1m0/QqSd7Ez9RDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b6e2f51244d7b25fe7846c96ba9f082880fcfb59df9cd79781f432186f5c5d4","last_reissued_at":"2026-07-05T08:38:41.869859Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:41.869859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Blind Inversion using Latent Diffusion Priors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"He Sun, Siyi Chen, Weimin Bai, Wenzheng Chen","submitted_at":"2024-07-01T07:23:28Z","abstract_excerpt":"Diffusion models have emerged as powerful tools for solving inverse problems due to their exceptional ability to model complex prior distributions. However, existing methods predominantly assume known forward operators (i.e., non-blind), limiting their applicability in practical settings where acquiring such operators is costly. Additionally, many current approaches rely on pixel-space diffusion models, leaving the potential of more powerful latent diffusion models (LDMs) underexplored. In this paper, we introduce LatentDEM, an innovative technique that addresses more challenging blind inverse"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.01027","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/2407.01027/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":"2407.01027","created_at":"2026-07-05T08:38:41.869916+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.01027v1","created_at":"2026-07-05T08:38:41.869916+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.01027","created_at":"2026-07-05T08:38:41.869916+00:00"},{"alias_kind":"pith_short_12","alias_value":"PNXC6UJEJV5S","created_at":"2026-07-05T08:38:41.869916+00:00"},{"alias_kind":"pith_short_16","alias_value":"PNXC6UJEJV5SL7TY","created_at":"2026-07-05T08:38:41.869916+00:00"},{"alias_kind":"pith_short_8","alias_value":"PNXC6UJE","created_at":"2026-07-05T08:38:41.869916+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/PNXC6UJEJV5SL7TYI3EWXKPQQK","json":"https://pith.science/pith/PNXC6UJEJV5SL7TYI3EWXKPQQK.json","graph_json":"https://pith.science/api/pith-number/PNXC6UJEJV5SL7TYI3EWXKPQQK/graph.json","events_json":"https://pith.science/api/pith-number/PNXC6UJEJV5SL7TYI3EWXKPQQK/events.json","paper":"https://pith.science/paper/PNXC6UJE"},"agent_actions":{"view_html":"https://pith.science/pith/PNXC6UJEJV5SL7TYI3EWXKPQQK","download_json":"https://pith.science/pith/PNXC6UJEJV5SL7TYI3EWXKPQQK.json","view_paper":"https://pith.science/paper/PNXC6UJE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.01027&json=true","fetch_graph":"https://pith.science/api/pith-number/PNXC6UJEJV5SL7TYI3EWXKPQQK/graph.json","fetch_events":"https://pith.science/api/pith-number/PNXC6UJEJV5SL7TYI3EWXKPQQK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PNXC6UJEJV5SL7TYI3EWXKPQQK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PNXC6UJEJV5SL7TYI3EWXKPQQK/action/storage_attestation","attest_author":"https://pith.science/pith/PNXC6UJEJV5SL7TYI3EWXKPQQK/action/author_attestation","sign_citation":"https://pith.science/pith/PNXC6UJEJV5SL7TYI3EWXKPQQK/action/citation_signature","submit_replication":"https://pith.science/pith/PNXC6UJEJV5SL7TYI3EWXKPQQK/action/replication_record"}},"created_at":"2026-07-05T08:38:41.869916+00:00","updated_at":"2026-07-05T08:38:41.869916+00:00"}