{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:EFSSACCP24XPZ7NVCNCULTPCT5","short_pith_number":"pith:EFSSACCP","canonical_record":{"source":{"id":"1706.05791","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-19T05:29:29Z","cross_cats_sorted":[],"title_canon_sha256":"aecc5e7b44523bc48a98ec601784ba1740800de132b67f99401a081680c6dbb7","abstract_canon_sha256":"29b15227321adedaf25f1e035a1cee0215671798c75d46c820f72229c22675f2"},"schema_version":"1.0"},"canonical_sha256":"216520084fd72efcfdb5134545cde29f4c2e8f4cc84c74827abf547923298159","source":{"kind":"arxiv","id":"1706.05791","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05791","created_at":"2026-05-18T00:42:08Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05791v1","created_at":"2026-05-18T00:42:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05791","created_at":"2026-05-18T00:42:08Z"},{"alias_kind":"pith_short_12","alias_value":"EFSSACCP24XP","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EFSSACCP24XPZ7NV","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EFSSACCP","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:EFSSACCP24XPZ7NVCNCULTPCT5","target":"record","payload":{"canonical_record":{"source":{"id":"1706.05791","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-19T05:29:29Z","cross_cats_sorted":[],"title_canon_sha256":"aecc5e7b44523bc48a98ec601784ba1740800de132b67f99401a081680c6dbb7","abstract_canon_sha256":"29b15227321adedaf25f1e035a1cee0215671798c75d46c820f72229c22675f2"},"schema_version":"1.0"},"canonical_sha256":"216520084fd72efcfdb5134545cde29f4c2e8f4cc84c74827abf547923298159","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:08.881171Z","signature_b64":"euGpk+zAnluCU6Y2kArr51RVipGVLW6+GpgdKoknu4pWeVcFbIUgk5oc0HHIl9EXZQ0UJK0SY14chwAffmTaAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"216520084fd72efcfdb5134545cde29f4c2e8f4cc84c74827abf547923298159","last_reissued_at":"2026-05-18T00:42:08.880771Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:08.880771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.05791","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s5Vqox/RgrgAHT528SeHuQFHQDETh4Z4a7E/3/uN1Dn7+MureCTlYUhc2Mcl8r3VpSiHTOhtHG4kzcm4gG/WBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T01:27:54.328463Z"},"content_sha256":"e9859b76e0ac332d6b6fb8324f7d528a2ac142733e081e0b305a34e5ff30ee9b","schema_version":"1.0","event_id":"sha256:e9859b76e0ac332d6b6fb8324f7d528a2ac142733e081e0b305a34e5ff30ee9b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:EFSSACCP24XPZ7NVCNCULTPCT5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Entropy-based Pruning Method for CNN Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian-Hao Luo, Jianxin Wu","submitted_at":"2017-06-19T05:29:29Z","abstract_excerpt":"This paper aims to simultaneously accelerate and compress off-the-shelf CNN models via filter pruning strategy. The importance of each filter is evaluated by the proposed entropy-based method first. Then several unimportant filters are discarded to get a smaller CNN model. Finally, fine-tuning is adopted to recover its generalization ability which is damaged during filter pruning. Our method can reduce the size of intermediate activations, which would dominate most memory footprint during model training stage but is less concerned in previous compression methods. Experiments on the ILSVRC-12 b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05791","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/4y1NfNedC7T3BI5hYMJtVSKs8gPteJ5yW7yJIAj+vsEs1BeWEd+N0jpm4dlviteobFPGRP3OuppQgwseA20Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T01:27:54.328811Z"},"content_sha256":"de62a4abf3983b0b7bbe0da102910f692b86d7c6e5300c2d313a60432d81d3af","schema_version":"1.0","event_id":"sha256:de62a4abf3983b0b7bbe0da102910f692b86d7c6e5300c2d313a60432d81d3af"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EFSSACCP24XPZ7NVCNCULTPCT5/bundle.json","state_url":"https://pith.science/pith/EFSSACCP24XPZ7NVCNCULTPCT5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EFSSACCP24XPZ7NVCNCULTPCT5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-28T01:27:54Z","links":{"resolver":"https://pith.science/pith/EFSSACCP24XPZ7NVCNCULTPCT5","bundle":"https://pith.science/pith/EFSSACCP24XPZ7NVCNCULTPCT5/bundle.json","state":"https://pith.science/pith/EFSSACCP24XPZ7NVCNCULTPCT5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EFSSACCP24XPZ7NVCNCULTPCT5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:EFSSACCP24XPZ7NVCNCULTPCT5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"29b15227321adedaf25f1e035a1cee0215671798c75d46c820f72229c22675f2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-19T05:29:29Z","title_canon_sha256":"aecc5e7b44523bc48a98ec601784ba1740800de132b67f99401a081680c6dbb7"},"schema_version":"1.0","source":{"id":"1706.05791","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05791","created_at":"2026-05-18T00:42:08Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05791v1","created_at":"2026-05-18T00:42:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05791","created_at":"2026-05-18T00:42:08Z"},{"alias_kind":"pith_short_12","alias_value":"EFSSACCP24XP","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EFSSACCP24XPZ7NV","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EFSSACCP","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:de62a4abf3983b0b7bbe0da102910f692b86d7c6e5300c2d313a60432d81d3af","target":"graph","created_at":"2026-05-18T00:42:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"This paper aims to simultaneously accelerate and compress off-the-shelf CNN models via filter pruning strategy. The importance of each filter is evaluated by the proposed entropy-based method first. Then several unimportant filters are discarded to get a smaller CNN model. Finally, fine-tuning is adopted to recover its generalization ability which is damaged during filter pruning. Our method can reduce the size of intermediate activations, which would dominate most memory footprint during model training stage but is less concerned in previous compression methods. Experiments on the ILSVRC-12 b","authors_text":"Jian-Hao Luo, Jianxin Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-19T05:29:29Z","title":"An Entropy-based Pruning Method for CNN Compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05791","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e9859b76e0ac332d6b6fb8324f7d528a2ac142733e081e0b305a34e5ff30ee9b","target":"record","created_at":"2026-05-18T00:42:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"29b15227321adedaf25f1e035a1cee0215671798c75d46c820f72229c22675f2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-19T05:29:29Z","title_canon_sha256":"aecc5e7b44523bc48a98ec601784ba1740800de132b67f99401a081680c6dbb7"},"schema_version":"1.0","source":{"id":"1706.05791","kind":"arxiv","version":1}},"canonical_sha256":"216520084fd72efcfdb5134545cde29f4c2e8f4cc84c74827abf547923298159","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"216520084fd72efcfdb5134545cde29f4c2e8f4cc84c74827abf547923298159","first_computed_at":"2026-05-18T00:42:08.880771Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:08.880771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"euGpk+zAnluCU6Y2kArr51RVipGVLW6+GpgdKoknu4pWeVcFbIUgk5oc0HHIl9EXZQ0UJK0SY14chwAffmTaAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:08.881171Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.05791","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e9859b76e0ac332d6b6fb8324f7d528a2ac142733e081e0b305a34e5ff30ee9b","sha256:de62a4abf3983b0b7bbe0da102910f692b86d7c6e5300c2d313a60432d81d3af"],"state_sha256":"408c684ac21b2f8b236b67c9d54a4c53ea966d2fbdcdc6ff8ab4fc3ca665abdd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OcJl6lYN4YdqnNw3SZxTM1sWH6WN9g1FWOA8qcKgAXECggyh/Gqi/oDYRlOG1AWnYfrqrbmAS8k1wuHWZjU9DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T01:27:54.330759Z","bundle_sha256":"b88d939a01e3146f68744c5335554f75c05cfc6c6b4c51f13392107d9906d902"}}