{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:J5EX77SXFA25V5EW7RTI5NDWHX","short_pith_number":"pith:J5EX77SX","schema_version":"1.0","canonical_sha256":"4f497ffe572835daf496fc668eb4763dd5dda1bec7d38ab87fb8eda045a1e043","source":{"kind":"arxiv","id":"2305.16965","version":2},"attestation_state":"computed","paper":{"title":"Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Fei Yin, Gongye Liu, Haoze Sun, Jiayi Li, Yujiu Yang","submitted_at":"2023-05-26T14:20:36Z","abstract_excerpt":"Diffusion models have recently demonstrated an impressive ability to address inverse problems in an unsupervised manner. While existing methods primarily focus on modifying the posterior sampling process, the potential of the forward process remains largely unexplored. In this work, we propose Shortcut Sampling for Diffusion(SSD), a novel approach for solving inverse problems in a zero-shot manner. Instead of initiating from random noise, the core concept of SSD is to find a specific transitional state that bridges the measurement image y and the restored image x. By utilizing the shortcut pat"},"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":"2305.16965","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-26T14:20:36Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"f8b090476ec88b2aa5a13db69c8b2e0187943d6f9bff9f816e526299d4321a1d","abstract_canon_sha256":"349799562aa05b5c6e6895a58943e4d79e19e63725cb31092e2efb0894386b1d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:38.825051Z","signature_b64":"kgkvRxnkEnK1mCn3rOPDhVi01gZ5ZmSoXsWqHruPztPxJWfKKJ5Zq9GDj9cavaZtkFi7l27ujWZTJHdyIB4vDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f497ffe572835daf496fc668eb4763dd5dda1bec7d38ab87fb8eda045a1e043","last_reissued_at":"2026-07-05T08:14:38.824577Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:38.824577Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Fei Yin, Gongye Liu, Haoze Sun, Jiayi Li, Yujiu Yang","submitted_at":"2023-05-26T14:20:36Z","abstract_excerpt":"Diffusion models have recently demonstrated an impressive ability to address inverse problems in an unsupervised manner. While existing methods primarily focus on modifying the posterior sampling process, the potential of the forward process remains largely unexplored. In this work, we propose Shortcut Sampling for Diffusion(SSD), a novel approach for solving inverse problems in a zero-shot manner. Instead of initiating from random noise, the core concept of SSD is to find a specific transitional state that bridges the measurement image y and the restored image x. By utilizing the shortcut pat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16965","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/2305.16965/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":"2305.16965","created_at":"2026-07-05T08:14:38.824633+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.16965v2","created_at":"2026-07-05T08:14:38.824633+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16965","created_at":"2026-07-05T08:14:38.824633+00:00"},{"alias_kind":"pith_short_12","alias_value":"J5EX77SXFA25","created_at":"2026-07-05T08:14:38.824633+00:00"},{"alias_kind":"pith_short_16","alias_value":"J5EX77SXFA25V5EW","created_at":"2026-07-05T08:14:38.824633+00:00"},{"alias_kind":"pith_short_8","alias_value":"J5EX77SX","created_at":"2026-07-05T08:14:38.824633+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2507.18064","citing_title":"Adapting Large VLMs with Iterative and Manual Instructions for Generative Low-light Enhancement","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J5EX77SXFA25V5EW7RTI5NDWHX","json":"https://pith.science/pith/J5EX77SXFA25V5EW7RTI5NDWHX.json","graph_json":"https://pith.science/api/pith-number/J5EX77SXFA25V5EW7RTI5NDWHX/graph.json","events_json":"https://pith.science/api/pith-number/J5EX77SXFA25V5EW7RTI5NDWHX/events.json","paper":"https://pith.science/paper/J5EX77SX"},"agent_actions":{"view_html":"https://pith.science/pith/J5EX77SXFA25V5EW7RTI5NDWHX","download_json":"https://pith.science/pith/J5EX77SXFA25V5EW7RTI5NDWHX.json","view_paper":"https://pith.science/paper/J5EX77SX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.16965&json=true","fetch_graph":"https://pith.science/api/pith-number/J5EX77SXFA25V5EW7RTI5NDWHX/graph.json","fetch_events":"https://pith.science/api/pith-number/J5EX77SXFA25V5EW7RTI5NDWHX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J5EX77SXFA25V5EW7RTI5NDWHX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J5EX77SXFA25V5EW7RTI5NDWHX/action/storage_attestation","attest_author":"https://pith.science/pith/J5EX77SXFA25V5EW7RTI5NDWHX/action/author_attestation","sign_citation":"https://pith.science/pith/J5EX77SXFA25V5EW7RTI5NDWHX/action/citation_signature","submit_replication":"https://pith.science/pith/J5EX77SXFA25V5EW7RTI5NDWHX/action/replication_record"}},"created_at":"2026-07-05T08:14:38.824633+00:00","updated_at":"2026-07-05T08:14:38.824633+00:00"}