{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BJ6UAWMKPDL5N32GRNVAIDPVCD","short_pith_number":"pith:BJ6UAWMK","schema_version":"1.0","canonical_sha256":"0a7d40598a78d7d6ef468b6a040df510df087d40abcd4934ece1046bdacb0edc","source":{"kind":"arxiv","id":"2403.11674","version":3},"attestation_state":"computed","paper":{"title":"Towards Generalizing to Unseen Domains with Few Labels","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chamuditha Jayanga Galappaththige, Malitha Gunawardhana, Muhammad Haris Khan, Sanoojan Baliah","submitted_at":"2024-03-18T11:21:52Z","abstract_excerpt":"We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically, our aim is to obtain a model that learns domain-generalizable features by leveraging a limited subset of labelled data alongside a substantially larger pool of unlabeled data. Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting. Nevertheless, SSL methods have considerable room for performance improvement when compared to fully-supervised DG training. To tackle this underexplore"},"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":"2403.11674","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-18T11:21:52Z","cross_cats_sorted":[],"title_canon_sha256":"86e5ddb62ca37fb87f9edcf6a95d23073d8a6be2f7613e067295aeb3926d6ac2","abstract_canon_sha256":"5c58650a84cfc1850631cc25db1514e8408557d76ce1fcf3663b4b6984b476bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:16:26.027861Z","signature_b64":"O7/P/MMMpy1JNSktSZvTEr7nGaov7FGCeYjXOXJBARtO0zUmjbKYJrYARJzXqtcZMozshPbfbg8L2yh8ES/KAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a7d40598a78d7d6ef468b6a040df510df087d40abcd4934ece1046bdacb0edc","last_reissued_at":"2026-07-05T08:16:26.027369Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:16:26.027369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Generalizing to Unseen Domains with Few Labels","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chamuditha Jayanga Galappaththige, Malitha Gunawardhana, Muhammad Haris Khan, Sanoojan Baliah","submitted_at":"2024-03-18T11:21:52Z","abstract_excerpt":"We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically, our aim is to obtain a model that learns domain-generalizable features by leveraging a limited subset of labelled data alongside a substantially larger pool of unlabeled data. Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting. Nevertheless, SSL methods have considerable room for performance improvement when compared to fully-supervised DG training. To tackle this underexplore"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.11674","kind":"arxiv","version":3},"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/2403.11674/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":"2403.11674","created_at":"2026-07-05T08:16:26.027430+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.11674v3","created_at":"2026-07-05T08:16:26.027430+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.11674","created_at":"2026-07-05T08:16:26.027430+00:00"},{"alias_kind":"pith_short_12","alias_value":"BJ6UAWMKPDL5","created_at":"2026-07-05T08:16:26.027430+00:00"},{"alias_kind":"pith_short_16","alias_value":"BJ6UAWMKPDL5N32G","created_at":"2026-07-05T08:16:26.027430+00:00"},{"alias_kind":"pith_short_8","alias_value":"BJ6UAWMK","created_at":"2026-07-05T08:16:26.027430+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.20841","citing_title":"FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BJ6UAWMKPDL5N32GRNVAIDPVCD","json":"https://pith.science/pith/BJ6UAWMKPDL5N32GRNVAIDPVCD.json","graph_json":"https://pith.science/api/pith-number/BJ6UAWMKPDL5N32GRNVAIDPVCD/graph.json","events_json":"https://pith.science/api/pith-number/BJ6UAWMKPDL5N32GRNVAIDPVCD/events.json","paper":"https://pith.science/paper/BJ6UAWMK"},"agent_actions":{"view_html":"https://pith.science/pith/BJ6UAWMKPDL5N32GRNVAIDPVCD","download_json":"https://pith.science/pith/BJ6UAWMKPDL5N32GRNVAIDPVCD.json","view_paper":"https://pith.science/paper/BJ6UAWMK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.11674&json=true","fetch_graph":"https://pith.science/api/pith-number/BJ6UAWMKPDL5N32GRNVAIDPVCD/graph.json","fetch_events":"https://pith.science/api/pith-number/BJ6UAWMKPDL5N32GRNVAIDPVCD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BJ6UAWMKPDL5N32GRNVAIDPVCD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BJ6UAWMKPDL5N32GRNVAIDPVCD/action/storage_attestation","attest_author":"https://pith.science/pith/BJ6UAWMKPDL5N32GRNVAIDPVCD/action/author_attestation","sign_citation":"https://pith.science/pith/BJ6UAWMKPDL5N32GRNVAIDPVCD/action/citation_signature","submit_replication":"https://pith.science/pith/BJ6UAWMKPDL5N32GRNVAIDPVCD/action/replication_record"}},"created_at":"2026-07-05T08:16:26.027430+00:00","updated_at":"2026-07-05T08:16:26.027430+00:00"}