{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7IHDAXPFXCGXIKLS7TXIDT2VLL","short_pith_number":"pith:7IHDAXPF","schema_version":"1.0","canonical_sha256":"fa0e305de5b88d742972fcee81cf555ad8c0c0c1a61d6815ba5d2573abb0cf62","source":{"kind":"arxiv","id":"1905.06498","version":3},"attestation_state":"computed","paper":{"title":"Investigating Channel Pruning through Structural Redundancy Reduction -- A Statistical Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengcheng Li, Dali Wang, Hairong Qi, Xiangyang Wang, Zi Wang","submitted_at":"2019-05-16T02:10:05Z","abstract_excerpt":"Most existing channel pruning methods formulate the pruning task from a perspective of inefficiency reduction which iteratively rank and remove the least important filters, or find the set of filters that minimizes some reconstruction errors after pruning. In this work, we investigate the channel pruning from a new perspective with statistical modeling. We hypothesize that the number of filters at a certain layer reflects the level of 'redundancy' in that layer and thus formulate the pruning problem from the aspect of redundancy reduction. Based on both theoretic analysis and empirical studies"},"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":"1905.06498","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-16T02:10:05Z","cross_cats_sorted":[],"title_canon_sha256":"3e2ce162583e8440e2c82d77ffc28b7196aeab617bf5426aeb0516a99aa2a493","abstract_canon_sha256":"23b92c467f3476f22cc22b25769b0693c41efd65cd96dcbe96ab3c8b535225aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:32.024693Z","signature_b64":"WDk2Ci+g4C7/W2Gdbwf29iPv1PIGVXyVfEsftiJovx/v3xYkHBR90HEbYLXgn6ZnzlXS6nyPAsKm9r8qIaN4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa0e305de5b88d742972fcee81cf555ad8c0c0c1a61d6815ba5d2573abb0cf62","last_reissued_at":"2026-05-17T23:40:32.024035Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:32.024035Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Investigating Channel Pruning through Structural Redundancy Reduction -- A Statistical Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengcheng Li, Dali Wang, Hairong Qi, Xiangyang Wang, Zi Wang","submitted_at":"2019-05-16T02:10:05Z","abstract_excerpt":"Most existing channel pruning methods formulate the pruning task from a perspective of inefficiency reduction which iteratively rank and remove the least important filters, or find the set of filters that minimizes some reconstruction errors after pruning. In this work, we investigate the channel pruning from a new perspective with statistical modeling. We hypothesize that the number of filters at a certain layer reflects the level of 'redundancy' in that layer and thus formulate the pruning problem from the aspect of redundancy reduction. Based on both theoretic analysis and empirical studies"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.06498","kind":"arxiv","version":3},"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":"1905.06498","created_at":"2026-05-17T23:40:32.024130+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.06498v3","created_at":"2026-05-17T23:40:32.024130+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.06498","created_at":"2026-05-17T23:40:32.024130+00:00"},{"alias_kind":"pith_short_12","alias_value":"7IHDAXPFXCGX","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"7IHDAXPFXCGXIKLS","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"7IHDAXPF","created_at":"2026-05-18T12:33:12.712433+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/7IHDAXPFXCGXIKLS7TXIDT2VLL","json":"https://pith.science/pith/7IHDAXPFXCGXIKLS7TXIDT2VLL.json","graph_json":"https://pith.science/api/pith-number/7IHDAXPFXCGXIKLS7TXIDT2VLL/graph.json","events_json":"https://pith.science/api/pith-number/7IHDAXPFXCGXIKLS7TXIDT2VLL/events.json","paper":"https://pith.science/paper/7IHDAXPF"},"agent_actions":{"view_html":"https://pith.science/pith/7IHDAXPFXCGXIKLS7TXIDT2VLL","download_json":"https://pith.science/pith/7IHDAXPFXCGXIKLS7TXIDT2VLL.json","view_paper":"https://pith.science/paper/7IHDAXPF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.06498&json=true","fetch_graph":"https://pith.science/api/pith-number/7IHDAXPFXCGXIKLS7TXIDT2VLL/graph.json","fetch_events":"https://pith.science/api/pith-number/7IHDAXPFXCGXIKLS7TXIDT2VLL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7IHDAXPFXCGXIKLS7TXIDT2VLL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7IHDAXPFXCGXIKLS7TXIDT2VLL/action/storage_attestation","attest_author":"https://pith.science/pith/7IHDAXPFXCGXIKLS7TXIDT2VLL/action/author_attestation","sign_citation":"https://pith.science/pith/7IHDAXPFXCGXIKLS7TXIDT2VLL/action/citation_signature","submit_replication":"https://pith.science/pith/7IHDAXPFXCGXIKLS7TXIDT2VLL/action/replication_record"}},"created_at":"2026-05-17T23:40:32.024130+00:00","updated_at":"2026-05-17T23:40:32.024130+00:00"}