{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:24KGRLZOV55VSJBDCFOYWMVTON","short_pith_number":"pith:24KGRLZO","schema_version":"1.0","canonical_sha256":"d71468af2eaf7b592423115d8b32b373568bb934bb8339ec693cb43e27d907e5","source":{"kind":"arxiv","id":"2105.12939","version":1},"attestation_state":"computed","paper":{"title":"Unsupervised Adaptive Semantic Segmentation with Local Lipschitz Constraint","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanyu Cai, Lianghua He","submitted_at":"2021-05-27T04:28:45Z","abstract_excerpt":"Recent advances in unsupervised domain adaptation have seen considerable progress in semantic segmentation. Existing methods either align different domains with adversarial training or involve the self-learning that utilizes pseudo labels to conduct supervised training. The former always suffers from the unstable training caused by adversarial training and only focuses on the inter-domain gap that ignores intra-domain knowledge. The latter tends to put overconfident label prediction on wrong categories, which propagates errors to more samples. To solve these problems, we propose a two-stage ad"},"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":"2105.12939","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-05-27T04:28:45Z","cross_cats_sorted":[],"title_canon_sha256":"45b2b0a3864c50e79bb84da087346b6201615d66be4f6b151d2a47a93b0854b5","abstract_canon_sha256":"1edade663a5f115ac514c7a66e9658b231dffa3dec08c5ad7d45d9564eded8bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:43:55.452710Z","signature_b64":"WMwP9s/0VTstmFbENLQRQByLBHnZ/iwXK8qJ2r0Q4MNogwwkxCX7Vffn8noI+J+xjbHL39hi/HsXdV/fIu78DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d71468af2eaf7b592423115d8b32b373568bb934bb8339ec693cb43e27d907e5","last_reissued_at":"2026-07-05T02:43:55.452267Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:43:55.452267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Adaptive Semantic Segmentation with Local Lipschitz Constraint","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanyu Cai, Lianghua He","submitted_at":"2021-05-27T04:28:45Z","abstract_excerpt":"Recent advances in unsupervised domain adaptation have seen considerable progress in semantic segmentation. Existing methods either align different domains with adversarial training or involve the self-learning that utilizes pseudo labels to conduct supervised training. The former always suffers from the unstable training caused by adversarial training and only focuses on the inter-domain gap that ignores intra-domain knowledge. The latter tends to put overconfident label prediction on wrong categories, which propagates errors to more samples. To solve these problems, we propose a two-stage ad"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.12939","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/2105.12939/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":"2105.12939","created_at":"2026-07-05T02:43:55.452326+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.12939v1","created_at":"2026-07-05T02:43:55.452326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.12939","created_at":"2026-07-05T02:43:55.452326+00:00"},{"alias_kind":"pith_short_12","alias_value":"24KGRLZOV55V","created_at":"2026-07-05T02:43:55.452326+00:00"},{"alias_kind":"pith_short_16","alias_value":"24KGRLZOV55VSJBD","created_at":"2026-07-05T02:43:55.452326+00:00"},{"alias_kind":"pith_short_8","alias_value":"24KGRLZO","created_at":"2026-07-05T02:43:55.452326+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/24KGRLZOV55VSJBDCFOYWMVTON","json":"https://pith.science/pith/24KGRLZOV55VSJBDCFOYWMVTON.json","graph_json":"https://pith.science/api/pith-number/24KGRLZOV55VSJBDCFOYWMVTON/graph.json","events_json":"https://pith.science/api/pith-number/24KGRLZOV55VSJBDCFOYWMVTON/events.json","paper":"https://pith.science/paper/24KGRLZO"},"agent_actions":{"view_html":"https://pith.science/pith/24KGRLZOV55VSJBDCFOYWMVTON","download_json":"https://pith.science/pith/24KGRLZOV55VSJBDCFOYWMVTON.json","view_paper":"https://pith.science/paper/24KGRLZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.12939&json=true","fetch_graph":"https://pith.science/api/pith-number/24KGRLZOV55VSJBDCFOYWMVTON/graph.json","fetch_events":"https://pith.science/api/pith-number/24KGRLZOV55VSJBDCFOYWMVTON/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/24KGRLZOV55VSJBDCFOYWMVTON/action/timestamp_anchor","attest_storage":"https://pith.science/pith/24KGRLZOV55VSJBDCFOYWMVTON/action/storage_attestation","attest_author":"https://pith.science/pith/24KGRLZOV55VSJBDCFOYWMVTON/action/author_attestation","sign_citation":"https://pith.science/pith/24KGRLZOV55VSJBDCFOYWMVTON/action/citation_signature","submit_replication":"https://pith.science/pith/24KGRLZOV55VSJBDCFOYWMVTON/action/replication_record"}},"created_at":"2026-07-05T02:43:55.452326+00:00","updated_at":"2026-07-05T02:43:55.452326+00:00"}