{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:5S46LWTG3PXGTC3RLDME46VYO2","short_pith_number":"pith:5S46LWTG","schema_version":"1.0","canonical_sha256":"ecb9e5da66dbee698b7158d84e7ab876830e8e26464fe4941c0f2bc85920606c","source":{"kind":"arxiv","id":"1710.10386","version":3},"attestation_state":"computed","paper":{"title":"Dual Skipping Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Changmao Cheng, Jianfeng Feng, Wei Liu, Wenlian Lu, Xiangyang Xue, Yanwei Fu, Yu-Gang Jiang","submitted_at":"2017-10-28T04:18:11Z","abstract_excerpt":"Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-to-fine object categorization. Such a network has two branches to simultaneously deal with both coarse and fine-grained classification tasks. Specifically, we propose a layer-skipping mechanism that learns a gating network to predict which layers to skip in the testing stage. This layer-skipping mechanism endows the network with good flexibility and capability in practice. Eval"},"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":"1710.10386","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-28T04:18:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5239fae1f09d99cbcc7bd622afd4cfa4f0991602f0a2bc1bed7f204668122ded","abstract_canon_sha256":"943cf2aa48967a65c9e284a34fe3e1c3a29ae7733e641f822d5d323ec093fc9e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:54.331881Z","signature_b64":"Lzj3josHwhCHLTGLC4RvXdXp48RsXDwyIxRfbnbnzdPSruPPbkxVLouCaUp5CGfkMgZL/8UBuZ4QO/chJcF6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ecb9e5da66dbee698b7158d84e7ab876830e8e26464fe4941c0f2bc85920606c","last_reissued_at":"2026-05-18T00:14:54.331115Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:54.331115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dual Skipping Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Changmao Cheng, Jianfeng Feng, Wei Liu, Wenlian Lu, Xiangyang Xue, Yanwei Fu, Yu-Gang Jiang","submitted_at":"2017-10-28T04:18:11Z","abstract_excerpt":"Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-to-fine object categorization. Such a network has two branches to simultaneously deal with both coarse and fine-grained classification tasks. Specifically, we propose a layer-skipping mechanism that learns a gating network to predict which layers to skip in the testing stage. This layer-skipping mechanism endows the network with good flexibility and capability in practice. Eval"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10386","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":"1710.10386","created_at":"2026-05-18T00:14:54.331229+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.10386v3","created_at":"2026-05-18T00:14:54.331229+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10386","created_at":"2026-05-18T00:14:54.331229+00:00"},{"alias_kind":"pith_short_12","alias_value":"5S46LWTG3PXG","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"5S46LWTG3PXGTC3R","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"5S46LWTG","created_at":"2026-05-18T12:31:00.734936+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/5S46LWTG3PXGTC3RLDME46VYO2","json":"https://pith.science/pith/5S46LWTG3PXGTC3RLDME46VYO2.json","graph_json":"https://pith.science/api/pith-number/5S46LWTG3PXGTC3RLDME46VYO2/graph.json","events_json":"https://pith.science/api/pith-number/5S46LWTG3PXGTC3RLDME46VYO2/events.json","paper":"https://pith.science/paper/5S46LWTG"},"agent_actions":{"view_html":"https://pith.science/pith/5S46LWTG3PXGTC3RLDME46VYO2","download_json":"https://pith.science/pith/5S46LWTG3PXGTC3RLDME46VYO2.json","view_paper":"https://pith.science/paper/5S46LWTG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.10386&json=true","fetch_graph":"https://pith.science/api/pith-number/5S46LWTG3PXGTC3RLDME46VYO2/graph.json","fetch_events":"https://pith.science/api/pith-number/5S46LWTG3PXGTC3RLDME46VYO2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5S46LWTG3PXGTC3RLDME46VYO2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5S46LWTG3PXGTC3RLDME46VYO2/action/storage_attestation","attest_author":"https://pith.science/pith/5S46LWTG3PXGTC3RLDME46VYO2/action/author_attestation","sign_citation":"https://pith.science/pith/5S46LWTG3PXGTC3RLDME46VYO2/action/citation_signature","submit_replication":"https://pith.science/pith/5S46LWTG3PXGTC3RLDME46VYO2/action/replication_record"}},"created_at":"2026-05-18T00:14:54.331229+00:00","updated_at":"2026-05-18T00:14:54.331229+00:00"}