{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:D2HOP4HTJPRIZIAI4BWZ3QYIGX","short_pith_number":"pith:D2HOP4HT","schema_version":"1.0","canonical_sha256":"1e8ee7f0f34be28ca008e06d9dc30835d38bb3849107a6ec26bcda02ef61a120","source":{"kind":"arxiv","id":"2307.10974","version":4},"attestation_state":"computed","paper":{"title":"Deep Multi-Threshold Spiking-UNet for Image Processing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","eess.IV"],"primary_cat":"cs.NE","authors_text":"Hebei Li, Xiaoyan Sun, Yueyi Zhang, Zhiwei Xiong","submitted_at":"2023-07-20T16:00:19Z","abstract_excerpt":"U-Net, known for its simple yet efficient architecture, is widely utilized for image processing tasks and is particularly suitable for deployment on neuromorphic chips. This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. To address the issue of information loss, we introduce multi-threshol"},"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":"2307.10974","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2023-07-20T16:00:19Z","cross_cats_sorted":["cs.CV","eess.IV"],"title_canon_sha256":"cadeed52ff9a487429d5fca8f712b87ed91692eab9c52ebcdbd295821dba099f","abstract_canon_sha256":"dda5c79d4b64a4981088e4c71a7641d578f9735217c7018903c290c33f32b7dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:06:41.758778Z","signature_b64":"beYmL4Bxez0vKHr+sTfVMonZ3t0wKkIuoYc7EQvw7H0TMCyOh3ACH9Oxw1LZ/DYHVqoN7L8j0gjculLx5HeGDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e8ee7f0f34be28ca008e06d9dc30835d38bb3849107a6ec26bcda02ef61a120","last_reissued_at":"2026-07-05T08:06:41.758331Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:06:41.758331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Multi-Threshold Spiking-UNet for Image Processing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","eess.IV"],"primary_cat":"cs.NE","authors_text":"Hebei Li, Xiaoyan Sun, Yueyi Zhang, Zhiwei Xiong","submitted_at":"2023-07-20T16:00:19Z","abstract_excerpt":"U-Net, known for its simple yet efficient architecture, is widely utilized for image processing tasks and is particularly suitable for deployment on neuromorphic chips. This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. To address the issue of information loss, we introduce multi-threshol"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.10974","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/2307.10974/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":"2307.10974","created_at":"2026-07-05T08:06:41.758402+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.10974v4","created_at":"2026-07-05T08:06:41.758402+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.10974","created_at":"2026-07-05T08:06:41.758402+00:00"},{"alias_kind":"pith_short_12","alias_value":"D2HOP4HTJPRI","created_at":"2026-07-05T08:06:41.758402+00:00"},{"alias_kind":"pith_short_16","alias_value":"D2HOP4HTJPRIZIAI","created_at":"2026-07-05T08:06:41.758402+00:00"},{"alias_kind":"pith_short_8","alias_value":"D2HOP4HT","created_at":"2026-07-05T08:06:41.758402+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/D2HOP4HTJPRIZIAI4BWZ3QYIGX","json":"https://pith.science/pith/D2HOP4HTJPRIZIAI4BWZ3QYIGX.json","graph_json":"https://pith.science/api/pith-number/D2HOP4HTJPRIZIAI4BWZ3QYIGX/graph.json","events_json":"https://pith.science/api/pith-number/D2HOP4HTJPRIZIAI4BWZ3QYIGX/events.json","paper":"https://pith.science/paper/D2HOP4HT"},"agent_actions":{"view_html":"https://pith.science/pith/D2HOP4HTJPRIZIAI4BWZ3QYIGX","download_json":"https://pith.science/pith/D2HOP4HTJPRIZIAI4BWZ3QYIGX.json","view_paper":"https://pith.science/paper/D2HOP4HT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.10974&json=true","fetch_graph":"https://pith.science/api/pith-number/D2HOP4HTJPRIZIAI4BWZ3QYIGX/graph.json","fetch_events":"https://pith.science/api/pith-number/D2HOP4HTJPRIZIAI4BWZ3QYIGX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D2HOP4HTJPRIZIAI4BWZ3QYIGX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D2HOP4HTJPRIZIAI4BWZ3QYIGX/action/storage_attestation","attest_author":"https://pith.science/pith/D2HOP4HTJPRIZIAI4BWZ3QYIGX/action/author_attestation","sign_citation":"https://pith.science/pith/D2HOP4HTJPRIZIAI4BWZ3QYIGX/action/citation_signature","submit_replication":"https://pith.science/pith/D2HOP4HTJPRIZIAI4BWZ3QYIGX/action/replication_record"}},"created_at":"2026-07-05T08:06:41.758402+00:00","updated_at":"2026-07-05T08:06:41.758402+00:00"}