{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:UHHH6Z6WCEFOUU6F6RWEPRFGPG","short_pith_number":"pith:UHHH6Z6W","schema_version":"1.0","canonical_sha256":"a1ce7f67d6110aea53c5f46c47c4a679aa180e030de87f7d8c84b65250560e6a","source":{"kind":"arxiv","id":"2203.13591","version":1},"attestation_state":"computed","paper":{"title":"Continual Test-Time Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dengxin Dai, Luc Van Gool, Olga Fink, Qin Wang","submitted_at":"2022-03-25T11:42:02Z","abstract_excerpt":"Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in non-stationary and continually changing environments where the target domain distribution can change over time. Existing methods, which are mostly based on self-training and entropy regularization, can suffer from these non-stationary environments. Due to the distribution shift over time in the target domain, pseudo-labels become unreliable. "},"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":"2203.13591","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-25T11:42:02Z","cross_cats_sorted":[],"title_canon_sha256":"f2621070ebc790ec6aaeea5defdbebaffb0d70168caa494327b4d4573ac41d13","abstract_canon_sha256":"e0d43ff0fece7bf8d309ac9684274b8a49ed5e0fd8c152013ae00674b5198959"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:08:34.271668Z","signature_b64":"qD0oYnexy+O8DvcxQQMeGIGqwZw/5mY2i3GrrQ4+YYEea9KvT2wHvi7Zr4rHg2s6sOzDJlX58Xziv6KQADDFCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1ce7f67d6110aea53c5f46c47c4a679aa180e030de87f7d8c84b65250560e6a","last_reissued_at":"2026-07-05T04:08:34.271264Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:08:34.271264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Continual Test-Time Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dengxin Dai, Luc Van Gool, Olga Fink, Qin Wang","submitted_at":"2022-03-25T11:42:02Z","abstract_excerpt":"Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in non-stationary and continually changing environments where the target domain distribution can change over time. Existing methods, which are mostly based on self-training and entropy regularization, can suffer from these non-stationary environments. Due to the distribution shift over time in the target domain, pseudo-labels become unreliable. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.13591","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/2203.13591/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":"2203.13591","created_at":"2026-07-05T04:08:34.271321+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.13591v1","created_at":"2026-07-05T04:08:34.271321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.13591","created_at":"2026-07-05T04:08:34.271321+00:00"},{"alias_kind":"pith_short_12","alias_value":"UHHH6Z6WCEFO","created_at":"2026-07-05T04:08:34.271321+00:00"},{"alias_kind":"pith_short_16","alias_value":"UHHH6Z6WCEFOUU6F","created_at":"2026-07-05T04:08:34.271321+00:00"},{"alias_kind":"pith_short_8","alias_value":"UHHH6Z6W","created_at":"2026-07-05T04:08:34.271321+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.24602","citing_title":"Majorization-Guided Test-Time Adaptation for Vision-Language Models under Modality-Specific Shift","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UHHH6Z6WCEFOUU6F6RWEPRFGPG","json":"https://pith.science/pith/UHHH6Z6WCEFOUU6F6RWEPRFGPG.json","graph_json":"https://pith.science/api/pith-number/UHHH6Z6WCEFOUU6F6RWEPRFGPG/graph.json","events_json":"https://pith.science/api/pith-number/UHHH6Z6WCEFOUU6F6RWEPRFGPG/events.json","paper":"https://pith.science/paper/UHHH6Z6W"},"agent_actions":{"view_html":"https://pith.science/pith/UHHH6Z6WCEFOUU6F6RWEPRFGPG","download_json":"https://pith.science/pith/UHHH6Z6WCEFOUU6F6RWEPRFGPG.json","view_paper":"https://pith.science/paper/UHHH6Z6W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.13591&json=true","fetch_graph":"https://pith.science/api/pith-number/UHHH6Z6WCEFOUU6F6RWEPRFGPG/graph.json","fetch_events":"https://pith.science/api/pith-number/UHHH6Z6WCEFOUU6F6RWEPRFGPG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UHHH6Z6WCEFOUU6F6RWEPRFGPG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UHHH6Z6WCEFOUU6F6RWEPRFGPG/action/storage_attestation","attest_author":"https://pith.science/pith/UHHH6Z6WCEFOUU6F6RWEPRFGPG/action/author_attestation","sign_citation":"https://pith.science/pith/UHHH6Z6WCEFOUU6F6RWEPRFGPG/action/citation_signature","submit_replication":"https://pith.science/pith/UHHH6Z6WCEFOUU6F6RWEPRFGPG/action/replication_record"}},"created_at":"2026-07-05T04:08:34.271321+00:00","updated_at":"2026-07-05T04:08:34.271321+00:00"}