{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:TD4CHFSNQN2LS3NQK6ZXJZQV2N","short_pith_number":"pith:TD4CHFSN","schema_version":"1.0","canonical_sha256":"98f823964d8374b96db057b374e615d35fef2e19bd45be07f004728ca5d50eb2","source":{"kind":"arxiv","id":"2012.15732","version":1},"attestation_state":"computed","paper":{"title":"Improving Unsupervised Domain Adaptation by Reducing Bi-level Feature Redundancy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Huibin Tan, Long Lan, Mengzhu Wang, Wei Wang, Xiang Zhang, Zhigang Luo","submitted_at":"2020-12-28T08:00:56Z","abstract_excerpt":"Reducing feature redundancy has shown beneficial effects for improving the accuracy of deep learning models, thus it is also indispensable for the models of unsupervised domain adaptation (UDA). Nevertheless, most recent efforts in the field of UDA ignores this point. Moreover, main schemes realizing this in general independent of UDA purely involve a single domain, thus might not be effective for cross-domain tasks. In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way. For the first level, we try to ensure compact domain-specific feat"},"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":"2012.15732","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-12-28T08:00:56Z","cross_cats_sorted":[],"title_canon_sha256":"287bedc8e0a503368b17e0501e82487b634e47cb82c9aa01c3bbc3e5d0c0acf5","abstract_canon_sha256":"067fadbfe3d2cb1ffd1c8ce348a96ed382b0f5b01d3d2d102b0fd590a29ff269"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:03:21.039566Z","signature_b64":"SGav967pPAVuNPMB7k8h2TSl/HEktiG4EYFHsBt50ZNhGM+DJnYq9ObMDOOfHO0jW6nAKFKh4Zme+3Vb7Y+xDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98f823964d8374b96db057b374e615d35fef2e19bd45be07f004728ca5d50eb2","last_reissued_at":"2026-07-05T02:03:21.039179Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:03:21.039179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Unsupervised Domain Adaptation by Reducing Bi-level Feature Redundancy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Huibin Tan, Long Lan, Mengzhu Wang, Wei Wang, Xiang Zhang, Zhigang Luo","submitted_at":"2020-12-28T08:00:56Z","abstract_excerpt":"Reducing feature redundancy has shown beneficial effects for improving the accuracy of deep learning models, thus it is also indispensable for the models of unsupervised domain adaptation (UDA). Nevertheless, most recent efforts in the field of UDA ignores this point. Moreover, main schemes realizing this in general independent of UDA purely involve a single domain, thus might not be effective for cross-domain tasks. In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way. For the first level, we try to ensure compact domain-specific feat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.15732","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/2012.15732/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":"2012.15732","created_at":"2026-07-05T02:03:21.039238+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.15732v1","created_at":"2026-07-05T02:03:21.039238+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.15732","created_at":"2026-07-05T02:03:21.039238+00:00"},{"alias_kind":"pith_short_12","alias_value":"TD4CHFSNQN2L","created_at":"2026-07-05T02:03:21.039238+00:00"},{"alias_kind":"pith_short_16","alias_value":"TD4CHFSNQN2LS3NQ","created_at":"2026-07-05T02:03:21.039238+00:00"},{"alias_kind":"pith_short_8","alias_value":"TD4CHFSN","created_at":"2026-07-05T02:03:21.039238+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/TD4CHFSNQN2LS3NQK6ZXJZQV2N","json":"https://pith.science/pith/TD4CHFSNQN2LS3NQK6ZXJZQV2N.json","graph_json":"https://pith.science/api/pith-number/TD4CHFSNQN2LS3NQK6ZXJZQV2N/graph.json","events_json":"https://pith.science/api/pith-number/TD4CHFSNQN2LS3NQK6ZXJZQV2N/events.json","paper":"https://pith.science/paper/TD4CHFSN"},"agent_actions":{"view_html":"https://pith.science/pith/TD4CHFSNQN2LS3NQK6ZXJZQV2N","download_json":"https://pith.science/pith/TD4CHFSNQN2LS3NQK6ZXJZQV2N.json","view_paper":"https://pith.science/paper/TD4CHFSN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.15732&json=true","fetch_graph":"https://pith.science/api/pith-number/TD4CHFSNQN2LS3NQK6ZXJZQV2N/graph.json","fetch_events":"https://pith.science/api/pith-number/TD4CHFSNQN2LS3NQK6ZXJZQV2N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TD4CHFSNQN2LS3NQK6ZXJZQV2N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TD4CHFSNQN2LS3NQK6ZXJZQV2N/action/storage_attestation","attest_author":"https://pith.science/pith/TD4CHFSNQN2LS3NQK6ZXJZQV2N/action/author_attestation","sign_citation":"https://pith.science/pith/TD4CHFSNQN2LS3NQK6ZXJZQV2N/action/citation_signature","submit_replication":"https://pith.science/pith/TD4CHFSNQN2LS3NQK6ZXJZQV2N/action/replication_record"}},"created_at":"2026-07-05T02:03:21.039238+00:00","updated_at":"2026-07-05T02:03:21.039238+00:00"}