{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4GS3IGH2ZQF3LNBIACDOBQE42Z","short_pith_number":"pith:4GS3IGH2","schema_version":"1.0","canonical_sha256":"e1a5b418facc0bb5b4280086e0c09cd651b6fbf9a949e905bf8a75c3b300ba5c","source":{"kind":"arxiv","id":"2505.21387","version":1},"attestation_state":"computed","paper":{"title":"Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"En Zhu, Fangdi Wang, Jiaqi Jin, Siwei Wang, Suyuan Liu, Xihong Yang, Xinwang Liu, Yue Liu, Yueming Jin","submitted_at":"2025-05-27T16:16:54Z","abstract_excerpt":"Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source information from diverse views in recent years. Most existing methods rely on the assumption of clean views. However, noise is pervasive in real-world scenarios, leading to a significant degradation in performance. To tackle this problem, we propose a novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC. Specifically, we reformulate noisy identification as "},"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":"2505.21387","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T16:16:54Z","cross_cats_sorted":[],"title_canon_sha256":"8d849a2fe70ef10f988bc3f8a2b721791287d194ccd807ecb51fd87b1f66476c","abstract_canon_sha256":"d406f52413733a082339e9d856d2514c9eff495dc85c87aa332ae1c246727d22"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:41.191703Z","signature_b64":"emZpcUwCQ2av2TvmhdeQZJcFP9kURyr4gQ7iVORMOze/1zBpgwdu4b7ZAPqGpcIY4IzHbWztD8gY+ERCiNtLDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1a5b418facc0bb5b4280086e0c09cd651b6fbf9a949e905bf8a75c3b300ba5c","last_reissued_at":"2026-07-05T11:10:41.191132Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:41.191132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"En Zhu, Fangdi Wang, Jiaqi Jin, Siwei Wang, Suyuan Liu, Xihong Yang, Xinwang Liu, Yue Liu, Yueming Jin","submitted_at":"2025-05-27T16:16:54Z","abstract_excerpt":"Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source information from diverse views in recent years. Most existing methods rely on the assumption of clean views. However, noise is pervasive in real-world scenarios, leading to a significant degradation in performance. To tackle this problem, we propose a novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC. Specifically, we reformulate noisy identification as "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21387","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/2505.21387/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":"2505.21387","created_at":"2026-07-05T11:10:41.191192+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.21387v1","created_at":"2026-07-05T11:10:41.191192+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21387","created_at":"2026-07-05T11:10:41.191192+00:00"},{"alias_kind":"pith_short_12","alias_value":"4GS3IGH2ZQF3","created_at":"2026-07-05T11:10:41.191192+00:00"},{"alias_kind":"pith_short_16","alias_value":"4GS3IGH2ZQF3LNBI","created_at":"2026-07-05T11:10:41.191192+00:00"},{"alias_kind":"pith_short_8","alias_value":"4GS3IGH2","created_at":"2026-07-05T11:10:41.191192+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/4GS3IGH2ZQF3LNBIACDOBQE42Z","json":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z.json","graph_json":"https://pith.science/api/pith-number/4GS3IGH2ZQF3LNBIACDOBQE42Z/graph.json","events_json":"https://pith.science/api/pith-number/4GS3IGH2ZQF3LNBIACDOBQE42Z/events.json","paper":"https://pith.science/paper/4GS3IGH2"},"agent_actions":{"view_html":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z","download_json":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z.json","view_paper":"https://pith.science/paper/4GS3IGH2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.21387&json=true","fetch_graph":"https://pith.science/api/pith-number/4GS3IGH2ZQF3LNBIACDOBQE42Z/graph.json","fetch_events":"https://pith.science/api/pith-number/4GS3IGH2ZQF3LNBIACDOBQE42Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/action/storage_attestation","attest_author":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/action/author_attestation","sign_citation":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/action/citation_signature","submit_replication":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/action/replication_record"}},"created_at":"2026-07-05T11:10:41.191192+00:00","updated_at":"2026-07-05T11:10:41.191192+00:00"}