{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:XWS4LESLCCBHAIE3T4ZZKYAIA2","short_pith_number":"pith:XWS4LESL","schema_version":"1.0","canonical_sha256":"bda5c5924b108270209b9f3395600806aa4224c13b0f771200e5fcf1abcab847","source":{"kind":"arxiv","id":"1610.09639","version":1},"attestation_state":"computed","paper":{"title":"Compact Deep Convolutional Neural Networks With Coarse Pruning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Sajid Anwar, Wonyong Sung","submitted_at":"2016-10-30T11:57:20Z","abstract_excerpt":"The learning capability of a neural network improves with increasing depth at higher computational costs. Wider layers with dense kernel connectivity patterns furhter increase this cost and may hinder real-time inference. We propose feature map and kernel level pruning for reducing the computational complexity of a deep convolutional neural network. Pruning feature maps reduces the width of a layer and hence does not need any sparse representation. Further, kernel pruning converts the dense connectivity pattern into a sparse one. Due to coarse nature, these pruning granularities can be exploit"},"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":"1610.09639","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-10-30T11:57:20Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"a458ab655f4ad2a89648473a3ade34fd9578cede4579c210897efe07fdd5a338","abstract_canon_sha256":"c8407ddbdde9a0c01ab5c33714ecab45d6b5291f3dfac0a3eefee5a71550faab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:51.413287Z","signature_b64":"xkBMPbaYJoSPNviiBBgn1Dxio1gq9oVSl76kRPAQxQK4R8IfPiP1iwicRsrYxKrskRZoV7HN3Ht4Nhh6PQeQAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bda5c5924b108270209b9f3395600806aa4224c13b0f771200e5fcf1abcab847","last_reissued_at":"2026-05-18T01:00:51.412645Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:51.412645Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Compact Deep Convolutional Neural Networks With Coarse Pruning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Sajid Anwar, Wonyong Sung","submitted_at":"2016-10-30T11:57:20Z","abstract_excerpt":"The learning capability of a neural network improves with increasing depth at higher computational costs. Wider layers with dense kernel connectivity patterns furhter increase this cost and may hinder real-time inference. We propose feature map and kernel level pruning for reducing the computational complexity of a deep convolutional neural network. Pruning feature maps reduces the width of a layer and hence does not need any sparse representation. Further, kernel pruning converts the dense connectivity pattern into a sparse one. Due to coarse nature, these pruning granularities can be exploit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.09639","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":""},"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":"1610.09639","created_at":"2026-05-18T01:00:51.412751+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.09639v1","created_at":"2026-05-18T01:00:51.412751+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.09639","created_at":"2026-05-18T01:00:51.412751+00:00"},{"alias_kind":"pith_short_12","alias_value":"XWS4LESLCCBH","created_at":"2026-05-18T12:30:51.357362+00:00"},{"alias_kind":"pith_short_16","alias_value":"XWS4LESLCCBHAIE3","created_at":"2026-05-18T12:30:51.357362+00:00"},{"alias_kind":"pith_short_8","alias_value":"XWS4LESL","created_at":"2026-05-18T12:30:51.357362+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/XWS4LESLCCBHAIE3T4ZZKYAIA2","json":"https://pith.science/pith/XWS4LESLCCBHAIE3T4ZZKYAIA2.json","graph_json":"https://pith.science/api/pith-number/XWS4LESLCCBHAIE3T4ZZKYAIA2/graph.json","events_json":"https://pith.science/api/pith-number/XWS4LESLCCBHAIE3T4ZZKYAIA2/events.json","paper":"https://pith.science/paper/XWS4LESL"},"agent_actions":{"view_html":"https://pith.science/pith/XWS4LESLCCBHAIE3T4ZZKYAIA2","download_json":"https://pith.science/pith/XWS4LESLCCBHAIE3T4ZZKYAIA2.json","view_paper":"https://pith.science/paper/XWS4LESL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.09639&json=true","fetch_graph":"https://pith.science/api/pith-number/XWS4LESLCCBHAIE3T4ZZKYAIA2/graph.json","fetch_events":"https://pith.science/api/pith-number/XWS4LESLCCBHAIE3T4ZZKYAIA2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XWS4LESLCCBHAIE3T4ZZKYAIA2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XWS4LESLCCBHAIE3T4ZZKYAIA2/action/storage_attestation","attest_author":"https://pith.science/pith/XWS4LESLCCBHAIE3T4ZZKYAIA2/action/author_attestation","sign_citation":"https://pith.science/pith/XWS4LESLCCBHAIE3T4ZZKYAIA2/action/citation_signature","submit_replication":"https://pith.science/pith/XWS4LESLCCBHAIE3T4ZZKYAIA2/action/replication_record"}},"created_at":"2026-05-18T01:00:51.412751+00:00","updated_at":"2026-05-18T01:00:51.412751+00:00"}