{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:7FIC3POLEXB6NKVFAKEWIZK32A","short_pith_number":"pith:7FIC3POL","schema_version":"1.0","canonical_sha256":"f9502dbdcb25c3e6aaa5028964655bd03da38417e3c30e99b1e1f385658cfa2f","source":{"kind":"arxiv","id":"1612.02649","version":1},"attestation_state":"computed","paper":{"title":"FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dequan Wang, Fisher Yu, Judy Hoffman, Trevor Darrell","submitted_at":"2016-12-08T14:11:10Z","abstract_excerpt":"Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a human observer. For example, training on one city and testing on another in a different geographic region and/or weather condition may result in significantly degraded performance due to pixel-level distribution shift. In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel pre"},"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":"1612.02649","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T14:11:10Z","cross_cats_sorted":[],"title_canon_sha256":"220272228a475986124bc574c722c9b1eb15bf4811e3cfbff1385eb7ce1ae5b9","abstract_canon_sha256":"5820ee37204064188229d4331c63e21df8460e0c2392e8e702285895f5c74f22"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:55:33.177138Z","signature_b64":"HUZrduCWrY3aRiABL5oltkXFoPVYt3Xt1tkaACPIQZRbPPuLCqVBbZkg4p2thyKACStjv9TB4umGOyBu79JoAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9502dbdcb25c3e6aaa5028964655bd03da38417e3c30e99b1e1f385658cfa2f","last_reissued_at":"2026-05-18T00:55:33.176663Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:55:33.176663Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dequan Wang, Fisher Yu, Judy Hoffman, Trevor Darrell","submitted_at":"2016-12-08T14:11:10Z","abstract_excerpt":"Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a human observer. For example, training on one city and testing on another in a different geographic region and/or weather condition may result in significantly degraded performance due to pixel-level distribution shift. In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel pre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02649","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":"1612.02649","created_at":"2026-05-18T00:55:33.176746+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.02649v1","created_at":"2026-05-18T00:55:33.176746+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02649","created_at":"2026-05-18T00:55:33.176746+00:00"},{"alias_kind":"pith_short_12","alias_value":"7FIC3POLEXB6","created_at":"2026-05-18T12:30:04.600751+00:00"},{"alias_kind":"pith_short_16","alias_value":"7FIC3POLEXB6NKVF","created_at":"2026-05-18T12:30:04.600751+00:00"},{"alias_kind":"pith_short_8","alias_value":"7FIC3POL","created_at":"2026-05-18T12:30:04.600751+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.08045","citing_title":"Adapting Foundation Models for Annotation-Efficient Adnexal Mass Segmentation in Cine Images","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7FIC3POLEXB6NKVFAKEWIZK32A","json":"https://pith.science/pith/7FIC3POLEXB6NKVFAKEWIZK32A.json","graph_json":"https://pith.science/api/pith-number/7FIC3POLEXB6NKVFAKEWIZK32A/graph.json","events_json":"https://pith.science/api/pith-number/7FIC3POLEXB6NKVFAKEWIZK32A/events.json","paper":"https://pith.science/paper/7FIC3POL"},"agent_actions":{"view_html":"https://pith.science/pith/7FIC3POLEXB6NKVFAKEWIZK32A","download_json":"https://pith.science/pith/7FIC3POLEXB6NKVFAKEWIZK32A.json","view_paper":"https://pith.science/paper/7FIC3POL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.02649&json=true","fetch_graph":"https://pith.science/api/pith-number/7FIC3POLEXB6NKVFAKEWIZK32A/graph.json","fetch_events":"https://pith.science/api/pith-number/7FIC3POLEXB6NKVFAKEWIZK32A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7FIC3POLEXB6NKVFAKEWIZK32A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7FIC3POLEXB6NKVFAKEWIZK32A/action/storage_attestation","attest_author":"https://pith.science/pith/7FIC3POLEXB6NKVFAKEWIZK32A/action/author_attestation","sign_citation":"https://pith.science/pith/7FIC3POLEXB6NKVFAKEWIZK32A/action/citation_signature","submit_replication":"https://pith.science/pith/7FIC3POLEXB6NKVFAKEWIZK32A/action/replication_record"}},"created_at":"2026-05-18T00:55:33.176746+00:00","updated_at":"2026-05-18T00:55:33.176746+00:00"}