{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:Q7MJPY2EF4LANOOQZ2EASQ7VEM","short_pith_number":"pith:Q7MJPY2E","schema_version":"1.0","canonical_sha256":"87d897e3442f1606b9d0ce880943f5232fe7539a8d26fef894b317a76889141d","source":{"kind":"arxiv","id":"1801.04261","version":2},"attestation_state":"computed","paper":{"title":"Deep saliency: What is learnt by a deep network about saliency?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nicolas Pugeault, Sen He","submitted_at":"2018-01-12T18:32:15Z","abstract_excerpt":"Deep convolutional neural networks have achieved impressive performance on a broad range of problems, beating prior art on established benchmarks, but it often remains unclear what are the representations learnt by those systems and how they achieve such performance. This article examines the specific problem of saliency detection, where benchmarks are currently dominated by CNN-based approaches, and investigates the properties of the learnt representation by visualizing the artificial neurons' receptive fields.\n  We demonstrate that fine tuning a pre-trained network on the saliency detection "},"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":"1801.04261","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-01-12T18:32:15Z","cross_cats_sorted":[],"title_canon_sha256":"5fe9f882133c640e0e801819eda2a7d6f89f953bca754fe89caaeec2e7585199","abstract_canon_sha256":"9fc560c5a0ca6b837916c0f7ef0542391203f6ed24fd71ea76d9717249c426f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:23.967768Z","signature_b64":"fnU7bJbuu8qNMbBbMMzEAjMCTjKic1sxONmExarnpcWKCTixtnp1ZUNn7k+MMaYKD/Lr7mmEnp+yVk+EqwWbAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"87d897e3442f1606b9d0ce880943f5232fe7539a8d26fef894b317a76889141d","last_reissued_at":"2026-05-18T00:20:23.967217Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:23.967217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep saliency: What is learnt by a deep network about saliency?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nicolas Pugeault, Sen He","submitted_at":"2018-01-12T18:32:15Z","abstract_excerpt":"Deep convolutional neural networks have achieved impressive performance on a broad range of problems, beating prior art on established benchmarks, but it often remains unclear what are the representations learnt by those systems and how they achieve such performance. This article examines the specific problem of saliency detection, where benchmarks are currently dominated by CNN-based approaches, and investigates the properties of the learnt representation by visualizing the artificial neurons' receptive fields.\n  We demonstrate that fine tuning a pre-trained network on the saliency detection "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.04261","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1801.04261","created_at":"2026-05-18T00:20:23.967302+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.04261v2","created_at":"2026-05-18T00:20:23.967302+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.04261","created_at":"2026-05-18T00:20:23.967302+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q7MJPY2EF4LA","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q7MJPY2EF4LANOOQ","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q7MJPY2E","created_at":"2026-05-18T12:32:46.962924+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/Q7MJPY2EF4LANOOQZ2EASQ7VEM","json":"https://pith.science/pith/Q7MJPY2EF4LANOOQZ2EASQ7VEM.json","graph_json":"https://pith.science/api/pith-number/Q7MJPY2EF4LANOOQZ2EASQ7VEM/graph.json","events_json":"https://pith.science/api/pith-number/Q7MJPY2EF4LANOOQZ2EASQ7VEM/events.json","paper":"https://pith.science/paper/Q7MJPY2E"},"agent_actions":{"view_html":"https://pith.science/pith/Q7MJPY2EF4LANOOQZ2EASQ7VEM","download_json":"https://pith.science/pith/Q7MJPY2EF4LANOOQZ2EASQ7VEM.json","view_paper":"https://pith.science/paper/Q7MJPY2E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.04261&json=true","fetch_graph":"https://pith.science/api/pith-number/Q7MJPY2EF4LANOOQZ2EASQ7VEM/graph.json","fetch_events":"https://pith.science/api/pith-number/Q7MJPY2EF4LANOOQZ2EASQ7VEM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q7MJPY2EF4LANOOQZ2EASQ7VEM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q7MJPY2EF4LANOOQZ2EASQ7VEM/action/storage_attestation","attest_author":"https://pith.science/pith/Q7MJPY2EF4LANOOQZ2EASQ7VEM/action/author_attestation","sign_citation":"https://pith.science/pith/Q7MJPY2EF4LANOOQZ2EASQ7VEM/action/citation_signature","submit_replication":"https://pith.science/pith/Q7MJPY2EF4LANOOQZ2EASQ7VEM/action/replication_record"}},"created_at":"2026-05-18T00:20:23.967302+00:00","updated_at":"2026-05-18T00:20:23.967302+00:00"}