{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:YUY35UE64X66BIEGM7Y6M5LOBO","short_pith_number":"pith:YUY35UE6","canonical_record":{"source":{"id":"2309.14157","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-25T14:08:45Z","cross_cats_sorted":[],"title_canon_sha256":"2e391f39d4872a897319f53d0fa668965fadd01e7d22e6d3e578d049e6161e27","abstract_canon_sha256":"60ad7ffe82c752a6632b7694d620460a1c9efc48647e9fded3b10c9d8fd1abfd"},"schema_version":"1.0"},"canonical_sha256":"c531bed09ee5fde0a08667f1e6756e0b834c5445c68f5610397d8c03011d2d11","source":{"kind":"arxiv","id":"2309.14157","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.14157","created_at":"2026-07-05T06:54:02Z"},{"alias_kind":"arxiv_version","alias_value":"2309.14157v1","created_at":"2026-07-05T06:54:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.14157","created_at":"2026-07-05T06:54:02Z"},{"alias_kind":"pith_short_12","alias_value":"YUY35UE64X66","created_at":"2026-07-05T06:54:02Z"},{"alias_kind":"pith_short_16","alias_value":"YUY35UE64X66BIEG","created_at":"2026-07-05T06:54:02Z"},{"alias_kind":"pith_short_8","alias_value":"YUY35UE6","created_at":"2026-07-05T06:54:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:YUY35UE64X66BIEGM7Y6M5LOBO","target":"record","payload":{"canonical_record":{"source":{"id":"2309.14157","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-25T14:08:45Z","cross_cats_sorted":[],"title_canon_sha256":"2e391f39d4872a897319f53d0fa668965fadd01e7d22e6d3e578d049e6161e27","abstract_canon_sha256":"60ad7ffe82c752a6632b7694d620460a1c9efc48647e9fded3b10c9d8fd1abfd"},"schema_version":"1.0"},"canonical_sha256":"c531bed09ee5fde0a08667f1e6756e0b834c5445c68f5610397d8c03011d2d11","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:54:02.274355Z","signature_b64":"LiTmkqAKAi8I72/HLExrUBumWmSUcZ7MeoXMD7FqJbK6DgFNUsOE2JvnzNGMn13uE5tuEXwWrK/vDB3nxQqcCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c531bed09ee5fde0a08667f1e6756e0b834c5445c68f5610397d8c03011d2d11","last_reissued_at":"2026-07-05T06:54:02.273952Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:54:02.273952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.14157","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":"2026-07-05T06:54:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zHcyz0j6ib6fid1dmDxEg5vdTwNbwpx+rB9dTbXBWkwnReSMqdTWgOpEnXruKBjECIwcUB1HLTK4BckerKAkDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T06:58:02.136613Z"},"content_sha256":"dc0843b2e60c27f4f6f24a30b668090e82ca80607375d1990454fa0d65a25107","schema_version":"1.0","event_id":"sha256:dc0843b2e60c27f4f6f24a30b668090e82ca80607375d1990454fa0d65a25107"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:YUY35UE64X66BIEGM7Y6M5LOBO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fang Liu, Kailing Guo, Pucheng Zhai, Xiangmin Xu, Xiaofen Xing","submitted_at":"2023-09-25T14:08:45Z","abstract_excerpt":"Structured pruning is a commonly used convolutional neural network (CNN) compression approach. Pruning rate setting is a fundamental problem in structured pruning. Most existing works introduce too many additional learnable parameters to assign different pruning rates across different layers in CNN or cannot control the compression rate explicitly. Since too narrow network blocks information flow for training, automatic pruning rate setting cannot explore a high pruning rate for a specific layer. To overcome these limitations, we propose a novel framework named Layer Adaptive Progressive Pruni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.14157","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/2309.14157/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:54:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OP1CsX7LeJdYqapia6LwnU+9x+r+mzkbWEnt0MnxHq4M5CgdEzLyvsec+33HFlHJ+79R7b3tMWiuPwUzlBlkDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T06:58:02.137477Z"},"content_sha256":"ea183c06b9d05e521d91bcf929a793ef3f0bac18ce4d5286cad8387812a7f721","schema_version":"1.0","event_id":"sha256:ea183c06b9d05e521d91bcf929a793ef3f0bac18ce4d5286cad8387812a7f721"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YUY35UE64X66BIEGM7Y6M5LOBO/bundle.json","state_url":"https://pith.science/pith/YUY35UE64X66BIEGM7Y6M5LOBO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YUY35UE64X66BIEGM7Y6M5LOBO/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":"2026-08-09T06:58:02Z","links":{"resolver":"https://pith.science/pith/YUY35UE64X66BIEGM7Y6M5LOBO","bundle":"https://pith.science/pith/YUY35UE64X66BIEGM7Y6M5LOBO/bundle.json","state":"https://pith.science/pith/YUY35UE64X66BIEGM7Y6M5LOBO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YUY35UE64X66BIEGM7Y6M5LOBO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YUY35UE64X66BIEGM7Y6M5LOBO","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":"60ad7ffe82c752a6632b7694d620460a1c9efc48647e9fded3b10c9d8fd1abfd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-25T14:08:45Z","title_canon_sha256":"2e391f39d4872a897319f53d0fa668965fadd01e7d22e6d3e578d049e6161e27"},"schema_version":"1.0","source":{"id":"2309.14157","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.14157","created_at":"2026-07-05T06:54:02Z"},{"alias_kind":"arxiv_version","alias_value":"2309.14157v1","created_at":"2026-07-05T06:54:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.14157","created_at":"2026-07-05T06:54:02Z"},{"alias_kind":"pith_short_12","alias_value":"YUY35UE64X66","created_at":"2026-07-05T06:54:02Z"},{"alias_kind":"pith_short_16","alias_value":"YUY35UE64X66BIEG","created_at":"2026-07-05T06:54:02Z"},{"alias_kind":"pith_short_8","alias_value":"YUY35UE6","created_at":"2026-07-05T06:54:02Z"}],"graph_snapshots":[{"event_id":"sha256:ea183c06b9d05e521d91bcf929a793ef3f0bac18ce4d5286cad8387812a7f721","target":"graph","created_at":"2026-07-05T06:54:02Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.14157/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Structured pruning is a commonly used convolutional neural network (CNN) compression approach. Pruning rate setting is a fundamental problem in structured pruning. Most existing works introduce too many additional learnable parameters to assign different pruning rates across different layers in CNN or cannot control the compression rate explicitly. Since too narrow network blocks information flow for training, automatic pruning rate setting cannot explore a high pruning rate for a specific layer. To overcome these limitations, we propose a novel framework named Layer Adaptive Progressive Pruni","authors_text":"Fang Liu, Kailing Guo, Pucheng Zhai, Xiangmin Xu, Xiaofen Xing","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-25T14:08:45Z","title":"LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.14157","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:dc0843b2e60c27f4f6f24a30b668090e82ca80607375d1990454fa0d65a25107","target":"record","created_at":"2026-07-05T06:54:02Z","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":"60ad7ffe82c752a6632b7694d620460a1c9efc48647e9fded3b10c9d8fd1abfd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-25T14:08:45Z","title_canon_sha256":"2e391f39d4872a897319f53d0fa668965fadd01e7d22e6d3e578d049e6161e27"},"schema_version":"1.0","source":{"id":"2309.14157","kind":"arxiv","version":1}},"canonical_sha256":"c531bed09ee5fde0a08667f1e6756e0b834c5445c68f5610397d8c03011d2d11","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c531bed09ee5fde0a08667f1e6756e0b834c5445c68f5610397d8c03011d2d11","first_computed_at":"2026-07-05T06:54:02.273952Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:54:02.273952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LiTmkqAKAi8I72/HLExrUBumWmSUcZ7MeoXMD7FqJbK6DgFNUsOE2JvnzNGMn13uE5tuEXwWrK/vDB3nxQqcCg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:54:02.274355Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.14157","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dc0843b2e60c27f4f6f24a30b668090e82ca80607375d1990454fa0d65a25107","sha256:ea183c06b9d05e521d91bcf929a793ef3f0bac18ce4d5286cad8387812a7f721"],"state_sha256":"1abc4123523775323a3b131dbc986a176f64946914e1b694bde75da76c53cacb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NhZ2u4JrFvBO2lOcZGMDokkw1Gg3QerxHmd0YqdN5HvQx5QXUULN4RhLICqC2vXx8X9/KgV24/OMFafNClSNBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T06:58:02.143955Z","bundle_sha256":"c4ec201505ed33a3226d1f40490c1f97e0d7d84e71b14a7e409f1280b3cae13a"}}