{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:UJKTZ4NZVUSXISW7GF3VM6F2RX","short_pith_number":"pith:UJKTZ4NZ","schema_version":"1.0","canonical_sha256":"a2553cf1b9ad25744adf31775678ba8de383bc13ee6238b18f60a0c77b9e83fe","source":{"kind":"arxiv","id":"2011.14356","version":1},"attestation_state":"computed","paper":{"title":"Layer Pruning via Fusible Residual Convolutional Block for Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fanhua Shang, Jian Cao, Pengtao Xu, Pu Li, Wenyu Sun","submitted_at":"2020-11-29T12:51:16Z","abstract_excerpt":"In order to deploy deep convolutional neural networks (CNNs) on resource-limited devices, many model pruning methods for filters and weights have been developed, while only a few to layer pruning. However, compared with filter pruning and weight pruning, the compact model obtained by layer pruning has less inference time and run-time memory usage when the same FLOPs and number of parameters are pruned because of less data moving in memory. In this paper, we propose a simple layer pruning method using fusible residual convolutional block (ResConv), which is implemented by inserting shortcut con"},"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":"2011.14356","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-29T12:51:16Z","cross_cats_sorted":[],"title_canon_sha256":"3727ec6f44e4223e74bd7b49d4c4505938521cd20e28ce10a9ca893ccec833a9","abstract_canon_sha256":"ac771dbfbc3d644794b1402aa72f9aaf28a0090ebd9fe4645d532cf065d6ad0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:55:19.867989Z","signature_b64":"RVVbQ2a7lC3aCv6OyfpbGCsnw57pcvkawsmbEsU11jEWc4mFJeYXBZlTR8HiRCFkeCJgPf9UUkyzNwjryMa+Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2553cf1b9ad25744adf31775678ba8de383bc13ee6238b18f60a0c77b9e83fe","last_reissued_at":"2026-07-05T01:55:19.867583Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:55:19.867583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Layer Pruning via Fusible Residual Convolutional Block for Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fanhua Shang, Jian Cao, Pengtao Xu, Pu Li, Wenyu Sun","submitted_at":"2020-11-29T12:51:16Z","abstract_excerpt":"In order to deploy deep convolutional neural networks (CNNs) on resource-limited devices, many model pruning methods for filters and weights have been developed, while only a few to layer pruning. However, compared with filter pruning and weight pruning, the compact model obtained by layer pruning has less inference time and run-time memory usage when the same FLOPs and number of parameters are pruned because of less data moving in memory. In this paper, we propose a simple layer pruning method using fusible residual convolutional block (ResConv), which is implemented by inserting shortcut con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.14356","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/2011.14356/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":"2011.14356","created_at":"2026-07-05T01:55:19.867638+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.14356v1","created_at":"2026-07-05T01:55:19.867638+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.14356","created_at":"2026-07-05T01:55:19.867638+00:00"},{"alias_kind":"pith_short_12","alias_value":"UJKTZ4NZVUSX","created_at":"2026-07-05T01:55:19.867638+00:00"},{"alias_kind":"pith_short_16","alias_value":"UJKTZ4NZVUSXISW7","created_at":"2026-07-05T01:55:19.867638+00:00"},{"alias_kind":"pith_short_8","alias_value":"UJKTZ4NZ","created_at":"2026-07-05T01:55:19.867638+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.11469","citing_title":"Structure-Aware Automatic Channel Pruning by Searching with Graph Embedding","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UJKTZ4NZVUSXISW7GF3VM6F2RX","json":"https://pith.science/pith/UJKTZ4NZVUSXISW7GF3VM6F2RX.json","graph_json":"https://pith.science/api/pith-number/UJKTZ4NZVUSXISW7GF3VM6F2RX/graph.json","events_json":"https://pith.science/api/pith-number/UJKTZ4NZVUSXISW7GF3VM6F2RX/events.json","paper":"https://pith.science/paper/UJKTZ4NZ"},"agent_actions":{"view_html":"https://pith.science/pith/UJKTZ4NZVUSXISW7GF3VM6F2RX","download_json":"https://pith.science/pith/UJKTZ4NZVUSXISW7GF3VM6F2RX.json","view_paper":"https://pith.science/paper/UJKTZ4NZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.14356&json=true","fetch_graph":"https://pith.science/api/pith-number/UJKTZ4NZVUSXISW7GF3VM6F2RX/graph.json","fetch_events":"https://pith.science/api/pith-number/UJKTZ4NZVUSXISW7GF3VM6F2RX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UJKTZ4NZVUSXISW7GF3VM6F2RX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UJKTZ4NZVUSXISW7GF3VM6F2RX/action/storage_attestation","attest_author":"https://pith.science/pith/UJKTZ4NZVUSXISW7GF3VM6F2RX/action/author_attestation","sign_citation":"https://pith.science/pith/UJKTZ4NZVUSXISW7GF3VM6F2RX/action/citation_signature","submit_replication":"https://pith.science/pith/UJKTZ4NZVUSXISW7GF3VM6F2RX/action/replication_record"}},"created_at":"2026-07-05T01:55:19.867638+00:00","updated_at":"2026-07-05T01:55:19.867638+00:00"}