{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT","short_pith_number":"pith:SK4Y2Z7Q","schema_version":"1.0","canonical_sha256":"92b98d67f0e3623515875e037de3cc04e1f79cd4ace129d726fe3d655c2b880e","source":{"kind":"arxiv","id":"2405.06277","version":1},"attestation_state":"computed","paper":{"title":"Learning A Spiking Neural Network for Efficient Image Deraining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guiyue Jin, Jiyu Jin, Kui Jiang, Pengpeng Li, Tianyu Song, Xiang Chen","submitted_at":"2024-05-10T07:19:58Z","abstract_excerpt":"Recently, spiking neural networks (SNNs) have demonstrated substantial potential in computer vision tasks. In this paper, we present an Efficient Spiking Deraining Network, called ESDNet. Our work is motivated by the observation that rain pixel values will lead to a more pronounced intensity of spike signals in SNNs. However, directly applying deep SNNs to image deraining task still remains a significant challenge. This is attributed to the information loss and training difficulties that arise from discrete binary activation and complex spatio-temporal dynamics. To this end, we develop a spiki"},"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":"2405.06277","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-10T07:19:58Z","cross_cats_sorted":[],"title_canon_sha256":"6d4f1f8d2090ffdb14b6c054da8d1b6a32c07dc29982a739bb14daf906d7d99a","abstract_canon_sha256":"967b254432aa6b96522922623c499000acd39171b5ba24b8bb0260d8cd3684bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:17:38.367643Z","signature_b64":"xYztESln6qqEUJWjxCeYO6NtB3IBYz05QaHsbIu879C+SSlITFKxNXm2bPOpyy1qsqfG3Tffv5ZSCjcaAgeABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92b98d67f0e3623515875e037de3cc04e1f79cd4ace129d726fe3d655c2b880e","last_reissued_at":"2026-07-05T08:17:38.367143Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:17:38.367143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning A Spiking Neural Network for Efficient Image Deraining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guiyue Jin, Jiyu Jin, Kui Jiang, Pengpeng Li, Tianyu Song, Xiang Chen","submitted_at":"2024-05-10T07:19:58Z","abstract_excerpt":"Recently, spiking neural networks (SNNs) have demonstrated substantial potential in computer vision tasks. In this paper, we present an Efficient Spiking Deraining Network, called ESDNet. Our work is motivated by the observation that rain pixel values will lead to a more pronounced intensity of spike signals in SNNs. However, directly applying deep SNNs to image deraining task still remains a significant challenge. This is attributed to the information loss and training difficulties that arise from discrete binary activation and complex spatio-temporal dynamics. To this end, we develop a spiki"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.06277","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/2405.06277/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":"2405.06277","created_at":"2026-07-05T08:17:38.367200+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.06277v1","created_at":"2026-07-05T08:17:38.367200+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.06277","created_at":"2026-07-05T08:17:38.367200+00:00"},{"alias_kind":"pith_short_12","alias_value":"SK4Y2Z7Q4NRD","created_at":"2026-07-05T08:17:38.367200+00:00"},{"alias_kind":"pith_short_16","alias_value":"SK4Y2Z7Q4NRDKFMH","created_at":"2026-07-05T08:17:38.367200+00:00"},{"alias_kind":"pith_short_8","alias_value":"SK4Y2Z7Q","created_at":"2026-07-05T08:17:38.367200+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.15808","citing_title":"ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT","json":"https://pith.science/pith/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT.json","graph_json":"https://pith.science/api/pith-number/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT/graph.json","events_json":"https://pith.science/api/pith-number/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT/events.json","paper":"https://pith.science/paper/SK4Y2Z7Q"},"agent_actions":{"view_html":"https://pith.science/pith/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT","download_json":"https://pith.science/pith/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT.json","view_paper":"https://pith.science/paper/SK4Y2Z7Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.06277&json=true","fetch_graph":"https://pith.science/api/pith-number/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT/graph.json","fetch_events":"https://pith.science/api/pith-number/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT/action/storage_attestation","attest_author":"https://pith.science/pith/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT/action/author_attestation","sign_citation":"https://pith.science/pith/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT/action/citation_signature","submit_replication":"https://pith.science/pith/SK4Y2Z7Q4NRDKFMHLYBX3Y6MAT/action/replication_record"}},"created_at":"2026-07-05T08:17:38.367200+00:00","updated_at":"2026-07-05T08:17:38.367200+00:00"}