{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZWR26ZUKUSVVYQILWD5YJQPHRU","short_pith_number":"pith:ZWR26ZUK","schema_version":"1.0","canonical_sha256":"cda3af668aa4ab5c410bb0fb84c1e78d37f17727b833c05a7c7655a319867be2","source":{"kind":"arxiv","id":"2401.09266","version":1},"attestation_state":"computed","paper":{"title":"P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuyu Zhang, Hui Ren, Xuming He","submitted_at":"2024-01-17T15:15:46Z","abstract_excerpt":"Deep clustering, which learns representation and semantic clustering without labels information, poses a great challenge for deep learning-based approaches. Despite significant progress in recent years, most existing methods focus on uniformly distributed datasets, significantly limiting the practical applicability of their methods. In this paper, we first introduce a more practical problem setting named deep imbalanced clustering, where the underlying classes exhibit an imbalance distribution. To tackle this problem, we propose a novel pseudo-labeling-based learning framework. Our framework f"},"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":"2401.09266","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-01-17T15:15:46Z","cross_cats_sorted":[],"title_canon_sha256":"cb2a7db304ba92da6d3b7a20dfd4829f40ec1f4238aff2a63131f7a853536307","abstract_canon_sha256":"d887b39029008d656aa7dd0cfec616f59890ea9bba353663c1aa93957cd99b51"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:34:41.482148Z","signature_b64":"VFdddldPW5H/751IItEJMik0x+hXH5F2+goNlMuXGCmDHpE5BJ+iNdRhL34XMtjYYZDNey5eaSiW/MHv90PRBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cda3af668aa4ab5c410bb0fb84c1e78d37f17727b833c05a7c7655a319867be2","last_reissued_at":"2026-07-05T07:34:41.481729Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:34:41.481729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuyu Zhang, Hui Ren, Xuming He","submitted_at":"2024-01-17T15:15:46Z","abstract_excerpt":"Deep clustering, which learns representation and semantic clustering without labels information, poses a great challenge for deep learning-based approaches. Despite significant progress in recent years, most existing methods focus on uniformly distributed datasets, significantly limiting the practical applicability of their methods. In this paper, we first introduce a more practical problem setting named deep imbalanced clustering, where the underlying classes exhibit an imbalance distribution. To tackle this problem, we propose a novel pseudo-labeling-based learning framework. Our framework f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.09266","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/2401.09266/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":"2401.09266","created_at":"2026-07-05T07:34:41.481781+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.09266v1","created_at":"2026-07-05T07:34:41.481781+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.09266","created_at":"2026-07-05T07:34:41.481781+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZWR26ZUKUSVV","created_at":"2026-07-05T07:34:41.481781+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZWR26ZUKUSVVYQIL","created_at":"2026-07-05T07:34:41.481781+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZWR26ZUK","created_at":"2026-07-05T07:34:41.481781+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29695","citing_title":"Progressive Self-Supervised Learning with Individualized Community Assignment for Brain Network Analysis","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZWR26ZUKUSVVYQILWD5YJQPHRU","json":"https://pith.science/pith/ZWR26ZUKUSVVYQILWD5YJQPHRU.json","graph_json":"https://pith.science/api/pith-number/ZWR26ZUKUSVVYQILWD5YJQPHRU/graph.json","events_json":"https://pith.science/api/pith-number/ZWR26ZUKUSVVYQILWD5YJQPHRU/events.json","paper":"https://pith.science/paper/ZWR26ZUK"},"agent_actions":{"view_html":"https://pith.science/pith/ZWR26ZUKUSVVYQILWD5YJQPHRU","download_json":"https://pith.science/pith/ZWR26ZUKUSVVYQILWD5YJQPHRU.json","view_paper":"https://pith.science/paper/ZWR26ZUK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.09266&json=true","fetch_graph":"https://pith.science/api/pith-number/ZWR26ZUKUSVVYQILWD5YJQPHRU/graph.json","fetch_events":"https://pith.science/api/pith-number/ZWR26ZUKUSVVYQILWD5YJQPHRU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZWR26ZUKUSVVYQILWD5YJQPHRU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZWR26ZUKUSVVYQILWD5YJQPHRU/action/storage_attestation","attest_author":"https://pith.science/pith/ZWR26ZUKUSVVYQILWD5YJQPHRU/action/author_attestation","sign_citation":"https://pith.science/pith/ZWR26ZUKUSVVYQILWD5YJQPHRU/action/citation_signature","submit_replication":"https://pith.science/pith/ZWR26ZUKUSVVYQILWD5YJQPHRU/action/replication_record"}},"created_at":"2026-07-05T07:34:41.481781+00:00","updated_at":"2026-07-05T07:34:41.481781+00:00"}