{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:B74Z222GJCE7C5POIXYAKHRQNN","short_pith_number":"pith:B74Z222G","schema_version":"1.0","canonical_sha256":"0ff99d6b464889f175ee45f0051e306b58ba92d7f91b6dc62b2254db707f7c54","source":{"kind":"arxiv","id":"1809.05298","version":1},"attestation_state":"computed","paper":{"title":"A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gijs Dubbelman, Panagiotis Meletis, Rob Romijnders","submitted_at":"2018-09-14T08:15:52Z","abstract_excerpt":"We propose a normalization layer for unsupervised domain adaption in semantic scene segmentation. Normalization layers are known to improve convergence and generalization and are part of many state-of-the-art fully-convolutional neural networks. We show that conventional normalization layers worsen the performance of current Unsupervised Adversarial Domain Adaption (UADA), which is a method to improve network performance on unlabeled datasets and the focus of our research. Therefore, we propose a novel Domain Agnostic Normalization layer and thereby unlock the benefits of normalization layers "},"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":"1809.05298","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-14T08:15:52Z","cross_cats_sorted":[],"title_canon_sha256":"381a41606de783ef06a1581d5a586f1121fb30ca1df7dba60428865a56cf0338","abstract_canon_sha256":"4da920af8801a086d1461786ba937f3cf59651dac48da806090a8f605d40f90a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:44.067664Z","signature_b64":"puQ78QGH/ELT1C45n+Zi+ZR6BKrAZjIHKxwNY20zVt6tuN7zyT3329pQg84ajHxjBIeTvdkGCQMrkw5WgWiIDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ff99d6b464889f175ee45f0051e306b58ba92d7f91b6dc62b2254db707f7c54","last_reissued_at":"2026-05-18T00:05:44.066907Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:44.066907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gijs Dubbelman, Panagiotis Meletis, Rob Romijnders","submitted_at":"2018-09-14T08:15:52Z","abstract_excerpt":"We propose a normalization layer for unsupervised domain adaption in semantic scene segmentation. Normalization layers are known to improve convergence and generalization and are part of many state-of-the-art fully-convolutional neural networks. We show that conventional normalization layers worsen the performance of current Unsupervised Adversarial Domain Adaption (UADA), which is a method to improve network performance on unlabeled datasets and the focus of our research. Therefore, we propose a novel Domain Agnostic Normalization layer and thereby unlock the benefits of normalization layers "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.05298","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":""},"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":"1809.05298","created_at":"2026-05-18T00:05:44.067011+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.05298v1","created_at":"2026-05-18T00:05:44.067011+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.05298","created_at":"2026-05-18T00:05:44.067011+00:00"},{"alias_kind":"pith_short_12","alias_value":"B74Z222GJCE7","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"B74Z222GJCE7C5PO","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"B74Z222G","created_at":"2026-05-18T12:32:13.499390+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/B74Z222GJCE7C5POIXYAKHRQNN","json":"https://pith.science/pith/B74Z222GJCE7C5POIXYAKHRQNN.json","graph_json":"https://pith.science/api/pith-number/B74Z222GJCE7C5POIXYAKHRQNN/graph.json","events_json":"https://pith.science/api/pith-number/B74Z222GJCE7C5POIXYAKHRQNN/events.json","paper":"https://pith.science/paper/B74Z222G"},"agent_actions":{"view_html":"https://pith.science/pith/B74Z222GJCE7C5POIXYAKHRQNN","download_json":"https://pith.science/pith/B74Z222GJCE7C5POIXYAKHRQNN.json","view_paper":"https://pith.science/paper/B74Z222G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.05298&json=true","fetch_graph":"https://pith.science/api/pith-number/B74Z222GJCE7C5POIXYAKHRQNN/graph.json","fetch_events":"https://pith.science/api/pith-number/B74Z222GJCE7C5POIXYAKHRQNN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B74Z222GJCE7C5POIXYAKHRQNN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B74Z222GJCE7C5POIXYAKHRQNN/action/storage_attestation","attest_author":"https://pith.science/pith/B74Z222GJCE7C5POIXYAKHRQNN/action/author_attestation","sign_citation":"https://pith.science/pith/B74Z222GJCE7C5POIXYAKHRQNN/action/citation_signature","submit_replication":"https://pith.science/pith/B74Z222GJCE7C5POIXYAKHRQNN/action/replication_record"}},"created_at":"2026-05-18T00:05:44.067011+00:00","updated_at":"2026-05-18T00:05:44.067011+00:00"}