{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:GPTACMW5DCZ67XEVD3N3N7I6AR","short_pith_number":"pith:GPTACMW5","schema_version":"1.0","canonical_sha256":"33e60132dd18b3efdc951edbb6fd1e0456b22500c1010c9dae7100f2f6d140dc","source":{"kind":"arxiv","id":"2009.04177","version":1},"attestation_state":"computed","paper":{"title":"MU-GAN: Facial Attribute Editing based on Multi-attention Mechanism","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Ke Zhang, Liang Qi, Xiwang Guo, Yukun Su, Zhenbing Zhao","submitted_at":"2020-09-09T09:25:04Z","abstract_excerpt":"Facial attribute editing has mainly two objectives: 1) translating image from a source domain to a target one, and 2) only changing the facial regions related to a target attribute and preserving the attribute-excluding details. In this work, we propose a Multi-attention U-Net-based Generative Adversarial Network (MU-GAN). First, we replace a classic convolutional encoder-decoder with a symmetric U-Net-like structure in a generator, and then apply an additive attention mechanism to build attention-based U-Net connections for adaptively transferring encoder representations to complement a decod"},"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":"2009.04177","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-09T09:25:04Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"cf5e44c2f6e662ba7473b82b74278ceaa6c48f397ae97bcc753ffc1398a6a1ef","abstract_canon_sha256":"0d878d55775e4519a35c2edb6c5f4a2bf13e22815ebcd149104ecd259ad5be42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:34:15.576371Z","signature_b64":"w7eUGaRhBGdcfvqy0nhyvbE4DomKDvixkVTftqmqtzxCyKcFCLBhq2sj56oaNa7y/wFBRTDppKDGF+zqvlbuDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33e60132dd18b3efdc951edbb6fd1e0456b22500c1010c9dae7100f2f6d140dc","last_reissued_at":"2026-07-05T01:34:15.575920Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:34:15.575920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MU-GAN: Facial Attribute Editing based on Multi-attention Mechanism","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Ke Zhang, Liang Qi, Xiwang Guo, Yukun Su, Zhenbing Zhao","submitted_at":"2020-09-09T09:25:04Z","abstract_excerpt":"Facial attribute editing has mainly two objectives: 1) translating image from a source domain to a target one, and 2) only changing the facial regions related to a target attribute and preserving the attribute-excluding details. In this work, we propose a Multi-attention U-Net-based Generative Adversarial Network (MU-GAN). First, we replace a classic convolutional encoder-decoder with a symmetric U-Net-like structure in a generator, and then apply an additive attention mechanism to build attention-based U-Net connections for adaptively transferring encoder representations to complement a decod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.04177","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/2009.04177/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":"2009.04177","created_at":"2026-07-05T01:34:15.575979+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.04177v1","created_at":"2026-07-05T01:34:15.575979+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.04177","created_at":"2026-07-05T01:34:15.575979+00:00"},{"alias_kind":"pith_short_12","alias_value":"GPTACMW5DCZ6","created_at":"2026-07-05T01:34:15.575979+00:00"},{"alias_kind":"pith_short_16","alias_value":"GPTACMW5DCZ67XEV","created_at":"2026-07-05T01:34:15.575979+00:00"},{"alias_kind":"pith_short_8","alias_value":"GPTACMW5","created_at":"2026-07-05T01:34:15.575979+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.01706","citing_title":"A modified Levenberg-Marquardt method for estimating the elastic material parameters of polymer waveguides using residuals between autocorrelated frequency responses","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GPTACMW5DCZ67XEVD3N3N7I6AR","json":"https://pith.science/pith/GPTACMW5DCZ67XEVD3N3N7I6AR.json","graph_json":"https://pith.science/api/pith-number/GPTACMW5DCZ67XEVD3N3N7I6AR/graph.json","events_json":"https://pith.science/api/pith-number/GPTACMW5DCZ67XEVD3N3N7I6AR/events.json","paper":"https://pith.science/paper/GPTACMW5"},"agent_actions":{"view_html":"https://pith.science/pith/GPTACMW5DCZ67XEVD3N3N7I6AR","download_json":"https://pith.science/pith/GPTACMW5DCZ67XEVD3N3N7I6AR.json","view_paper":"https://pith.science/paper/GPTACMW5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.04177&json=true","fetch_graph":"https://pith.science/api/pith-number/GPTACMW5DCZ67XEVD3N3N7I6AR/graph.json","fetch_events":"https://pith.science/api/pith-number/GPTACMW5DCZ67XEVD3N3N7I6AR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GPTACMW5DCZ67XEVD3N3N7I6AR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GPTACMW5DCZ67XEVD3N3N7I6AR/action/storage_attestation","attest_author":"https://pith.science/pith/GPTACMW5DCZ67XEVD3N3N7I6AR/action/author_attestation","sign_citation":"https://pith.science/pith/GPTACMW5DCZ67XEVD3N3N7I6AR/action/citation_signature","submit_replication":"https://pith.science/pith/GPTACMW5DCZ67XEVD3N3N7I6AR/action/replication_record"}},"created_at":"2026-07-05T01:34:15.575979+00:00","updated_at":"2026-07-05T01:34:15.575979+00:00"}