{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JDVWTBMETSMOFCIRWE4PQ52NG7","short_pith_number":"pith:JDVWTBME","schema_version":"1.0","canonical_sha256":"48eb6985849c98e28911b138f8774d37d2ef7d35bb37951a1856a58ddfcab279","source":{"kind":"arxiv","id":"2412.18105","version":1},"attestation_state":"computed","paper":{"title":"Beyond the Known: Enhancing Open Set Domain Adaptation with Unknown Exploration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jurandy Almeida, Lucas Fernando Alvarenga e Silva, Nicu Sebe, Samuel Felipe dos Santos","submitted_at":"2024-12-24T02:27:35Z","abstract_excerpt":"Convolutional neural networks (CNNs) can learn directly from raw data, resulting in exceptional performance across various research areas. However, factors present in non-controllable environments such as unlabeled datasets with varying levels of domain and category shift can reduce model accuracy. The Open Set Domain Adaptation (OSDA) is a challenging problem that arises when both of these issues occur together. Existing OSDA approaches in literature only align known classes or use supervised training to learn unknown classes as a single new category. In this work, we introduce a new approach"},"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":"2412.18105","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-24T02:27:35Z","cross_cats_sorted":[],"title_canon_sha256":"5c9920bf2b4c0f5330969ee00d1a7c862d02b7f6ed113330592f724c87bae0a0","abstract_canon_sha256":"1476c93112439a6205e119723fa43d119c4ca32421524e76875f5c9aec7208e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:49.921537Z","signature_b64":"1j/GR3t+p8doD4OsZe5A/MNqXAf2qPEs1T9upYfcwjvD6wzZdhnnhtepQQluHvkwNvqhVbvNNb+9wnvDh05/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48eb6985849c98e28911b138f8774d37d2ef7d35bb37951a1856a58ddfcab279","last_reissued_at":"2026-07-05T09:53:49.921084Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:49.921084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond the Known: Enhancing Open Set Domain Adaptation with Unknown Exploration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jurandy Almeida, Lucas Fernando Alvarenga e Silva, Nicu Sebe, Samuel Felipe dos Santos","submitted_at":"2024-12-24T02:27:35Z","abstract_excerpt":"Convolutional neural networks (CNNs) can learn directly from raw data, resulting in exceptional performance across various research areas. However, factors present in non-controllable environments such as unlabeled datasets with varying levels of domain and category shift can reduce model accuracy. The Open Set Domain Adaptation (OSDA) is a challenging problem that arises when both of these issues occur together. Existing OSDA approaches in literature only align known classes or use supervised training to learn unknown classes as a single new category. In this work, we introduce a new approach"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.18105","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/2412.18105/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":"2412.18105","created_at":"2026-07-05T09:53:49.921140+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.18105v1","created_at":"2026-07-05T09:53:49.921140+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.18105","created_at":"2026-07-05T09:53:49.921140+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDVWTBMETSMO","created_at":"2026-07-05T09:53:49.921140+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDVWTBMETSMOFCIR","created_at":"2026-07-05T09:53:49.921140+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDVWTBME","created_at":"2026-07-05T09:53:49.921140+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/JDVWTBMETSMOFCIRWE4PQ52NG7","json":"https://pith.science/pith/JDVWTBMETSMOFCIRWE4PQ52NG7.json","graph_json":"https://pith.science/api/pith-number/JDVWTBMETSMOFCIRWE4PQ52NG7/graph.json","events_json":"https://pith.science/api/pith-number/JDVWTBMETSMOFCIRWE4PQ52NG7/events.json","paper":"https://pith.science/paper/JDVWTBME"},"agent_actions":{"view_html":"https://pith.science/pith/JDVWTBMETSMOFCIRWE4PQ52NG7","download_json":"https://pith.science/pith/JDVWTBMETSMOFCIRWE4PQ52NG7.json","view_paper":"https://pith.science/paper/JDVWTBME","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.18105&json=true","fetch_graph":"https://pith.science/api/pith-number/JDVWTBMETSMOFCIRWE4PQ52NG7/graph.json","fetch_events":"https://pith.science/api/pith-number/JDVWTBMETSMOFCIRWE4PQ52NG7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDVWTBMETSMOFCIRWE4PQ52NG7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDVWTBMETSMOFCIRWE4PQ52NG7/action/storage_attestation","attest_author":"https://pith.science/pith/JDVWTBMETSMOFCIRWE4PQ52NG7/action/author_attestation","sign_citation":"https://pith.science/pith/JDVWTBMETSMOFCIRWE4PQ52NG7/action/citation_signature","submit_replication":"https://pith.science/pith/JDVWTBMETSMOFCIRWE4PQ52NG7/action/replication_record"}},"created_at":"2026-07-05T09:53:49.921140+00:00","updated_at":"2026-07-05T09:53:49.921140+00:00"}