{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZSOBCDUMJ624D6DONUQ3Q5RUXL","short_pith_number":"pith:ZSOBCDUM","schema_version":"1.0","canonical_sha256":"cc9c110e8c4fb5c1f86e6d21b87634baff4746655b890d12651e15107394b88f","source":{"kind":"arxiv","id":"2506.14176","version":1},"attestation_state":"computed","paper":{"title":"One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Renao Yan","submitted_at":"2025-06-17T04:37:02Z","abstract_excerpt":"Deep learning-based pathological image analysis presents unique challenges due to the practical constraints of network design. Most existing methods apply computer vision models directly to medical tasks, neglecting the distinct characteristics of pathological images. This mismatch often leads to computational inefficiencies, particularly in edge-computing scenarios. To address this, we propose a novel Network Similarity Directed Initialization (NSDI) strategy to improve the stability of neural architecture search (NAS). Furthermore, we introduce domain adaptation into one-shot NAS to better h"},"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":"2506.14176","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-17T04:37:02Z","cross_cats_sorted":[],"title_canon_sha256":"d0407237209c79d60b59e51b3600db831665bae3d80a65fc34812b50b08a7730","abstract_canon_sha256":"c792b3ea5c4ba7deeadee80def62a1df24eb6edb961a98136aadaecb618c817a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:50.344892Z","signature_b64":"FiaC/dPOA4dG/2v37hoRBAV20xFzq3dpZaaSIhT2OKx7a8wiE5ccGWj2nQW2J1iqFDzttWB7GWB+RhkhNjgfDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc9c110e8c4fb5c1f86e6d21b87634baff4746655b890d12651e15107394b88f","last_reissued_at":"2026-07-05T11:22:50.344404Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:50.344404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Renao Yan","submitted_at":"2025-06-17T04:37:02Z","abstract_excerpt":"Deep learning-based pathological image analysis presents unique challenges due to the practical constraints of network design. Most existing methods apply computer vision models directly to medical tasks, neglecting the distinct characteristics of pathological images. This mismatch often leads to computational inefficiencies, particularly in edge-computing scenarios. To address this, we propose a novel Network Similarity Directed Initialization (NSDI) strategy to improve the stability of neural architecture search (NAS). Furthermore, we introduce domain adaptation into one-shot NAS to better h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14176","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/2506.14176/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.14176","created_at":"2026-07-05T11:22:50.344475+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.14176v1","created_at":"2026-07-05T11:22:50.344475+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14176","created_at":"2026-07-05T11:22:50.344475+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZSOBCDUMJ624","created_at":"2026-07-05T11:22:50.344475+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZSOBCDUMJ624D6DO","created_at":"2026-07-05T11:22:50.344475+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZSOBCDUM","created_at":"2026-07-05T11:22:50.344475+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.21322","citing_title":"Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search","ref_index":51,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZSOBCDUMJ624D6DONUQ3Q5RUXL","json":"https://pith.science/pith/ZSOBCDUMJ624D6DONUQ3Q5RUXL.json","graph_json":"https://pith.science/api/pith-number/ZSOBCDUMJ624D6DONUQ3Q5RUXL/graph.json","events_json":"https://pith.science/api/pith-number/ZSOBCDUMJ624D6DONUQ3Q5RUXL/events.json","paper":"https://pith.science/paper/ZSOBCDUM"},"agent_actions":{"view_html":"https://pith.science/pith/ZSOBCDUMJ624D6DONUQ3Q5RUXL","download_json":"https://pith.science/pith/ZSOBCDUMJ624D6DONUQ3Q5RUXL.json","view_paper":"https://pith.science/paper/ZSOBCDUM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.14176&json=true","fetch_graph":"https://pith.science/api/pith-number/ZSOBCDUMJ624D6DONUQ3Q5RUXL/graph.json","fetch_events":"https://pith.science/api/pith-number/ZSOBCDUMJ624D6DONUQ3Q5RUXL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZSOBCDUMJ624D6DONUQ3Q5RUXL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZSOBCDUMJ624D6DONUQ3Q5RUXL/action/storage_attestation","attest_author":"https://pith.science/pith/ZSOBCDUMJ624D6DONUQ3Q5RUXL/action/author_attestation","sign_citation":"https://pith.science/pith/ZSOBCDUMJ624D6DONUQ3Q5RUXL/action/citation_signature","submit_replication":"https://pith.science/pith/ZSOBCDUMJ624D6DONUQ3Q5RUXL/action/replication_record"}},"created_at":"2026-07-05T11:22:50.344475+00:00","updated_at":"2026-07-05T11:22:50.344475+00:00"}