{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:V4VLFVQSCZYSJB2Q4K62C3BYGS","short_pith_number":"pith:V4VLFVQS","schema_version":"1.0","canonical_sha256":"af2ab2d6121671248750e2bda16c3834a1710243185e47d796bc764933e12711","source":{"kind":"arxiv","id":"1703.08050","version":3},"attestation_state":"computed","paper":{"title":"Is Second-order Information Helpful for Large-scale Visual Recognition?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiangtao Xie, Peihua Li, Qilong Wang, Wangmeng Zuo","submitted_at":"2017-03-23T12:55:34Z","abstract_excerpt":"By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets) effectively learn from low-level to high-level features and discriminative representations. Since the end goal of large-scale recognition is to delineate complex boundaries of thousands of classes, adequate exploration of feature distributions is important for realizing full potentials of ConvNets. However, state-of-the-art works concentrate only on deeper or wider architecture design, while rarely exploring feature statistics higher than first-order. We take a step towards addressing this problem. Our method"},"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":"1703.08050","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-23T12:55:34Z","cross_cats_sorted":[],"title_canon_sha256":"caf4cfb6bbabbff3edb04957fbe4cdcf85c9a83b4924b24e29b21de6515da2cb","abstract_canon_sha256":"4339d25c7b45ce3f95d578d4b1d703261910baf8ebe85f325d43a13ebbbbeaf7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:42.376283Z","signature_b64":"8pq6xNJCY/+cjg9ZabkZVXh9d69uG+UeWrki3jbwfyWbCiqqHBWBgrhAHTZ6casTm8G9EgWJTGNB3aJCKi6qDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af2ab2d6121671248750e2bda16c3834a1710243185e47d796bc764933e12711","last_reissued_at":"2026-05-18T00:19:42.375619Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:42.375619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Is Second-order Information Helpful for Large-scale Visual Recognition?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiangtao Xie, Peihua Li, Qilong Wang, Wangmeng Zuo","submitted_at":"2017-03-23T12:55:34Z","abstract_excerpt":"By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets) effectively learn from low-level to high-level features and discriminative representations. Since the end goal of large-scale recognition is to delineate complex boundaries of thousands of classes, adequate exploration of feature distributions is important for realizing full potentials of ConvNets. However, state-of-the-art works concentrate only on deeper or wider architecture design, while rarely exploring feature statistics higher than first-order. We take a step towards addressing this problem. Our method"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.08050","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":"1703.08050","created_at":"2026-05-18T00:19:42.375729+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.08050v3","created_at":"2026-05-18T00:19:42.375729+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.08050","created_at":"2026-05-18T00:19:42.375729+00:00"},{"alias_kind":"pith_short_12","alias_value":"V4VLFVQSCZYS","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"V4VLFVQSCZYSJB2Q","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"V4VLFVQS","created_at":"2026-05-18T12:31:49.984773+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/V4VLFVQSCZYSJB2Q4K62C3BYGS","json":"https://pith.science/pith/V4VLFVQSCZYSJB2Q4K62C3BYGS.json","graph_json":"https://pith.science/api/pith-number/V4VLFVQSCZYSJB2Q4K62C3BYGS/graph.json","events_json":"https://pith.science/api/pith-number/V4VLFVQSCZYSJB2Q4K62C3BYGS/events.json","paper":"https://pith.science/paper/V4VLFVQS"},"agent_actions":{"view_html":"https://pith.science/pith/V4VLFVQSCZYSJB2Q4K62C3BYGS","download_json":"https://pith.science/pith/V4VLFVQSCZYSJB2Q4K62C3BYGS.json","view_paper":"https://pith.science/paper/V4VLFVQS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.08050&json=true","fetch_graph":"https://pith.science/api/pith-number/V4VLFVQSCZYSJB2Q4K62C3BYGS/graph.json","fetch_events":"https://pith.science/api/pith-number/V4VLFVQSCZYSJB2Q4K62C3BYGS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V4VLFVQSCZYSJB2Q4K62C3BYGS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V4VLFVQSCZYSJB2Q4K62C3BYGS/action/storage_attestation","attest_author":"https://pith.science/pith/V4VLFVQSCZYSJB2Q4K62C3BYGS/action/author_attestation","sign_citation":"https://pith.science/pith/V4VLFVQSCZYSJB2Q4K62C3BYGS/action/citation_signature","submit_replication":"https://pith.science/pith/V4VLFVQSCZYSJB2Q4K62C3BYGS/action/replication_record"}},"created_at":"2026-05-18T00:19:42.375729+00:00","updated_at":"2026-05-18T00:19:42.375729+00:00"}