{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:AJCX5UXSXJBTSM25GTHEEKWIZN","short_pith_number":"pith:AJCX5UXS","canonical_record":{"source":{"id":"1810.05331","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-10-12T03:00:59Z","cross_cats_sorted":[],"title_canon_sha256":"5e5ac8a7470d375bfc1ebe21f9354541f610064155d38c06e17a0075539ad0b9","abstract_canon_sha256":"f67f640b2254de9e8621d2258e1795f42dea9edb43c39ed02479b8d769cd51b7"},"schema_version":"1.0"},"canonical_sha256":"02457ed2f2ba4339335d34ce422ac8cb6881302da48611cb3a21632d66f489e6","source":{"kind":"arxiv","id":"1810.05331","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.05331","created_at":"2026-05-17T23:55:28Z"},{"alias_kind":"arxiv_version","alias_value":"1810.05331v2","created_at":"2026-05-17T23:55:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.05331","created_at":"2026-05-17T23:55:28Z"},{"alias_kind":"pith_short_12","alias_value":"AJCX5UXSXJBT","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"AJCX5UXSXJBTSM25","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"AJCX5UXS","created_at":"2026-05-18T12:32:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:AJCX5UXSXJBTSM25GTHEEKWIZN","target":"record","payload":{"canonical_record":{"source":{"id":"1810.05331","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-10-12T03:00:59Z","cross_cats_sorted":[],"title_canon_sha256":"5e5ac8a7470d375bfc1ebe21f9354541f610064155d38c06e17a0075539ad0b9","abstract_canon_sha256":"f67f640b2254de9e8621d2258e1795f42dea9edb43c39ed02479b8d769cd51b7"},"schema_version":"1.0"},"canonical_sha256":"02457ed2f2ba4339335d34ce422ac8cb6881302da48611cb3a21632d66f489e6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:28.840204Z","signature_b64":"32dTNWbB9V7pqfcZhcgsxEzHNj9YIslhfL60ZJN7J+7dhYtJfYJ2oEyRO/aQZ9YESYg/MFLN1sPT8z9HOdqACw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02457ed2f2ba4339335d34ce422ac8cb6881302da48611cb3a21632d66f489e6","last_reissued_at":"2026-05-17T23:55:28.839480Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:28.839480Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.05331","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:55:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2DT6ogqZEyKLMNQiSE4XvveG9Bri5Cp6/ULoPBfFunlYRbFp/zd38MdVRyhnL4OV9Boum0xSL/s6DV5Iim9aAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:57:53.725003Z"},"content_sha256":"b745a55563a48cb2b216033778e50cc46edb34eea0696215a6dd1949d042941d","schema_version":"1.0","event_id":"sha256:b745a55563a48cb2b216033778e50cc46edb34eea0696215a6dd1949d042941d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:AJCX5UXSXJBTSM25GTHEEKWIZN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Channel Pruning: Feature Boosting and Suppression","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng-zhong Xu, {\\L}ukasz Dudziak, Robert Mullins, Xitong Gao, Yiren Zhao","submitted_at":"2018-10-12T03:00:59Z","abstract_excerpt":"Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time. FBS introduces small auxiliary connections to existing convolutional layers. In contrast to channel pruning methods which permanently remove channels, it pres"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.05331","kind":"arxiv","version":2},"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":"2026-05-17T23:55:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8Ytfhue8lestir0fwZvQd1OnGxNfzmXO1yRIg3X39bU4MUzG8uSos9NuVXqhtWoF5FcmLUwpETbpzNG+ytovCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:57:53.725406Z"},"content_sha256":"476f09bb8af309c7d9bd2e6ea9aebe0256ae00563bf3ce793b276b48559c18ee","schema_version":"1.0","event_id":"sha256:476f09bb8af309c7d9bd2e6ea9aebe0256ae00563bf3ce793b276b48559c18ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AJCX5UXSXJBTSM25GTHEEKWIZN/bundle.json","state_url":"https://pith.science/pith/AJCX5UXSXJBTSM25GTHEEKWIZN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AJCX5UXSXJBTSM25GTHEEKWIZN/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":"2026-05-25T20:57:53Z","links":{"resolver":"https://pith.science/pith/AJCX5UXSXJBTSM25GTHEEKWIZN","bundle":"https://pith.science/pith/AJCX5UXSXJBTSM25GTHEEKWIZN/bundle.json","state":"https://pith.science/pith/AJCX5UXSXJBTSM25GTHEEKWIZN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AJCX5UXSXJBTSM25GTHEEKWIZN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:AJCX5UXSXJBTSM25GTHEEKWIZN","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":"f67f640b2254de9e8621d2258e1795f42dea9edb43c39ed02479b8d769cd51b7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-10-12T03:00:59Z","title_canon_sha256":"5e5ac8a7470d375bfc1ebe21f9354541f610064155d38c06e17a0075539ad0b9"},"schema_version":"1.0","source":{"id":"1810.05331","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.05331","created_at":"2026-05-17T23:55:28Z"},{"alias_kind":"arxiv_version","alias_value":"1810.05331v2","created_at":"2026-05-17T23:55:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.05331","created_at":"2026-05-17T23:55:28Z"},{"alias_kind":"pith_short_12","alias_value":"AJCX5UXSXJBT","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"AJCX5UXSXJBTSM25","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"AJCX5UXS","created_at":"2026-05-18T12:32:13Z"}],"graph_snapshots":[{"event_id":"sha256:476f09bb8af309c7d9bd2e6ea9aebe0256ae00563bf3ce793b276b48559c18ee","target":"graph","created_at":"2026-05-17T23:55:28Z","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":"Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time. FBS introduces small auxiliary connections to existing convolutional layers. In contrast to channel pruning methods which permanently remove channels, it pres","authors_text":"Cheng-zhong Xu, {\\L}ukasz Dudziak, Robert Mullins, Xitong Gao, Yiren Zhao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-10-12T03:00:59Z","title":"Dynamic Channel Pruning: Feature Boosting and Suppression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.05331","kind":"arxiv","version":2},"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:b745a55563a48cb2b216033778e50cc46edb34eea0696215a6dd1949d042941d","target":"record","created_at":"2026-05-17T23:55:28Z","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":"f67f640b2254de9e8621d2258e1795f42dea9edb43c39ed02479b8d769cd51b7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-10-12T03:00:59Z","title_canon_sha256":"5e5ac8a7470d375bfc1ebe21f9354541f610064155d38c06e17a0075539ad0b9"},"schema_version":"1.0","source":{"id":"1810.05331","kind":"arxiv","version":2}},"canonical_sha256":"02457ed2f2ba4339335d34ce422ac8cb6881302da48611cb3a21632d66f489e6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"02457ed2f2ba4339335d34ce422ac8cb6881302da48611cb3a21632d66f489e6","first_computed_at":"2026-05-17T23:55:28.839480Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:55:28.839480Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"32dTNWbB9V7pqfcZhcgsxEzHNj9YIslhfL60ZJN7J+7dhYtJfYJ2oEyRO/aQZ9YESYg/MFLN1sPT8z9HOdqACw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:55:28.840204Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.05331","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b745a55563a48cb2b216033778e50cc46edb34eea0696215a6dd1949d042941d","sha256:476f09bb8af309c7d9bd2e6ea9aebe0256ae00563bf3ce793b276b48559c18ee"],"state_sha256":"c0acf85dfbbed7320695c42490ff2323729752477c842ebce3142a6b6174d0ff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3XFUENekYbcAtooO/vNQWvwzKiJDkW/B38DmiQb6GMuXz1bLPrENzxdfcwRfaUOwK26Vv0UxhkLZCESo4bVPBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T20:57:53.728068Z","bundle_sha256":"c4b55dad4b32b4615297c2650509521e290fa7671c9e7488eacd3edbdf781270"}}