{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:SWBJ2IO3MW2U6HN33WTMAWPETV","short_pith_number":"pith:SWBJ2IO3","schema_version":"1.0","canonical_sha256":"95829d21db65b54f1dbbdda6c059e49d7848f1a4e3fa42dbc8a3cfd73f4783b1","source":{"kind":"arxiv","id":"2109.02973","version":4},"attestation_state":"computed","paper":{"title":"Unpaired Deep Image Deraining Using Dual Contrastive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Caihua Kong, Jinshan Pan, Kui Jiang, Longgang Dai, Xiang Chen, Yufeng Huang, Yufeng Li, Zhentao Fan","submitted_at":"2021-09-07T10:00:45Z","abstract_excerpt":"Learning single image deraining (SID) networks from an unpaired set of clean and rainy images is practical and valuable as acquiring paired real-world data is almost infeasible. However, without the paired data as the supervision, learning a SID network is challenging. Moreover, simply using existing unpaired learning methods (e.g., unpaired adversarial learning and cycle-consistency constraints) in the SID task is insufficient to learn the underlying relationship from rainy inputs to clean outputs as there exists significant domain gap between the rainy and clean images. In this paper, we dev"},"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":"2109.02973","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-07T10:00:45Z","cross_cats_sorted":[],"title_canon_sha256":"b932a0cd5f437d951b1cc3466f21e75b4aa4cf241ab9e1ad8fcbaeb3eb6d49cf","abstract_canon_sha256":"0df22a3de909ea0c57532078142efbfedd66d63690de8925d2c99fc3cc986269"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:08:03.649477Z","signature_b64":"tSYjzSoiZgNiFdVa6Pdmaq/DqGDXMom0NCJNW+ZLT56f1gyVo3a3SDCSghnkl/3zOauOBoBQQ0KNpIcrq8eRDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95829d21db65b54f1dbbdda6c059e49d7848f1a4e3fa42dbc8a3cfd73f4783b1","last_reissued_at":"2026-07-05T04:08:03.649028Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:08:03.649028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unpaired Deep Image Deraining Using Dual Contrastive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Caihua Kong, Jinshan Pan, Kui Jiang, Longgang Dai, Xiang Chen, Yufeng Huang, Yufeng Li, Zhentao Fan","submitted_at":"2021-09-07T10:00:45Z","abstract_excerpt":"Learning single image deraining (SID) networks from an unpaired set of clean and rainy images is practical and valuable as acquiring paired real-world data is almost infeasible. However, without the paired data as the supervision, learning a SID network is challenging. Moreover, simply using existing unpaired learning methods (e.g., unpaired adversarial learning and cycle-consistency constraints) in the SID task is insufficient to learn the underlying relationship from rainy inputs to clean outputs as there exists significant domain gap between the rainy and clean images. In this paper, we dev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.02973","kind":"arxiv","version":4},"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/2109.02973/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":"2109.02973","created_at":"2026-07-05T04:08:03.649078+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.02973v4","created_at":"2026-07-05T04:08:03.649078+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.02973","created_at":"2026-07-05T04:08:03.649078+00:00"},{"alias_kind":"pith_short_12","alias_value":"SWBJ2IO3MW2U","created_at":"2026-07-05T04:08:03.649078+00:00"},{"alias_kind":"pith_short_16","alias_value":"SWBJ2IO3MW2U6HN3","created_at":"2026-07-05T04:08:03.649078+00:00"},{"alias_kind":"pith_short_8","alias_value":"SWBJ2IO3","created_at":"2026-07-05T04:08:03.649078+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/SWBJ2IO3MW2U6HN33WTMAWPETV","json":"https://pith.science/pith/SWBJ2IO3MW2U6HN33WTMAWPETV.json","graph_json":"https://pith.science/api/pith-number/SWBJ2IO3MW2U6HN33WTMAWPETV/graph.json","events_json":"https://pith.science/api/pith-number/SWBJ2IO3MW2U6HN33WTMAWPETV/events.json","paper":"https://pith.science/paper/SWBJ2IO3"},"agent_actions":{"view_html":"https://pith.science/pith/SWBJ2IO3MW2U6HN33WTMAWPETV","download_json":"https://pith.science/pith/SWBJ2IO3MW2U6HN33WTMAWPETV.json","view_paper":"https://pith.science/paper/SWBJ2IO3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.02973&json=true","fetch_graph":"https://pith.science/api/pith-number/SWBJ2IO3MW2U6HN33WTMAWPETV/graph.json","fetch_events":"https://pith.science/api/pith-number/SWBJ2IO3MW2U6HN33WTMAWPETV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SWBJ2IO3MW2U6HN33WTMAWPETV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SWBJ2IO3MW2U6HN33WTMAWPETV/action/storage_attestation","attest_author":"https://pith.science/pith/SWBJ2IO3MW2U6HN33WTMAWPETV/action/author_attestation","sign_citation":"https://pith.science/pith/SWBJ2IO3MW2U6HN33WTMAWPETV/action/citation_signature","submit_replication":"https://pith.science/pith/SWBJ2IO3MW2U6HN33WTMAWPETV/action/replication_record"}},"created_at":"2026-07-05T04:08:03.649078+00:00","updated_at":"2026-07-05T04:08:03.649078+00:00"}