{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:IXO6HIIOLWAD4MENIBRRQ4WUI4","short_pith_number":"pith:IXO6HIIO","schema_version":"1.0","canonical_sha256":"45dde3a10e5d803e308d40631872d44732432716e3c402ad31aa85127d93ecdd","source":{"kind":"arxiv","id":"2111.10591","version":1},"attestation_state":"computed","paper":{"title":"AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhishek Srivastava, Sukalpa Chanda, Umapada Pal","submitted_at":"2021-11-20T13:43:03Z","abstract_excerpt":"The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively. Even though the convolutional neural network and generative adversarial network-based methods deliver impressive performances on face hallucination tasks, the ability to use attributes associated with the low-resolution images to improve performance is unsatisfactory. In this paper, we propose an Attribute Guided Attention Generative Adversarial Network which employs novel attribute guided attention (AGA) modules to identify and focus the generation process "},"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":"2111.10591","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-20T13:43:03Z","cross_cats_sorted":[],"title_canon_sha256":"6d0e5aa024a1338234f1020b746d92b6201d9581631dcc336931dce0fbcc2f6b","abstract_canon_sha256":"bb0bbd1f4ee3e706eefaa7a0996e4897f875adc7ae65a8b67ccc1506db8f7b7d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:33:54.560327Z","signature_b64":"Ox8bV2WeBJeJhA6f1xZmI6uEQC8yJfbCBv5J6p6u+xsNNliGVg6VxC6XQd4wqsFGMLLVqdFnjZ4pIQt4YVeIBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45dde3a10e5d803e308d40631872d44732432716e3c402ad31aa85127d93ecdd","last_reissued_at":"2026-07-05T03:33:54.559916Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:33:54.559916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhishek Srivastava, Sukalpa Chanda, Umapada Pal","submitted_at":"2021-11-20T13:43:03Z","abstract_excerpt":"The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively. Even though the convolutional neural network and generative adversarial network-based methods deliver impressive performances on face hallucination tasks, the ability to use attributes associated with the low-resolution images to improve performance is unsatisfactory. In this paper, we propose an Attribute Guided Attention Generative Adversarial Network which employs novel attribute guided attention (AGA) modules to identify and focus the generation process "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.10591","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/2111.10591/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":"2111.10591","created_at":"2026-07-05T03:33:54.559969+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.10591v1","created_at":"2026-07-05T03:33:54.559969+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.10591","created_at":"2026-07-05T03:33:54.559969+00:00"},{"alias_kind":"pith_short_12","alias_value":"IXO6HIIOLWAD","created_at":"2026-07-05T03:33:54.559969+00:00"},{"alias_kind":"pith_short_16","alias_value":"IXO6HIIOLWAD4MEN","created_at":"2026-07-05T03:33:54.559969+00:00"},{"alias_kind":"pith_short_8","alias_value":"IXO6HIIO","created_at":"2026-07-05T03:33:54.559969+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/IXO6HIIOLWAD4MENIBRRQ4WUI4","json":"https://pith.science/pith/IXO6HIIOLWAD4MENIBRRQ4WUI4.json","graph_json":"https://pith.science/api/pith-number/IXO6HIIOLWAD4MENIBRRQ4WUI4/graph.json","events_json":"https://pith.science/api/pith-number/IXO6HIIOLWAD4MENIBRRQ4WUI4/events.json","paper":"https://pith.science/paper/IXO6HIIO"},"agent_actions":{"view_html":"https://pith.science/pith/IXO6HIIOLWAD4MENIBRRQ4WUI4","download_json":"https://pith.science/pith/IXO6HIIOLWAD4MENIBRRQ4WUI4.json","view_paper":"https://pith.science/paper/IXO6HIIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.10591&json=true","fetch_graph":"https://pith.science/api/pith-number/IXO6HIIOLWAD4MENIBRRQ4WUI4/graph.json","fetch_events":"https://pith.science/api/pith-number/IXO6HIIOLWAD4MENIBRRQ4WUI4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IXO6HIIOLWAD4MENIBRRQ4WUI4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IXO6HIIOLWAD4MENIBRRQ4WUI4/action/storage_attestation","attest_author":"https://pith.science/pith/IXO6HIIOLWAD4MENIBRRQ4WUI4/action/author_attestation","sign_citation":"https://pith.science/pith/IXO6HIIOLWAD4MENIBRRQ4WUI4/action/citation_signature","submit_replication":"https://pith.science/pith/IXO6HIIOLWAD4MENIBRRQ4WUI4/action/replication_record"}},"created_at":"2026-07-05T03:33:54.559969+00:00","updated_at":"2026-07-05T03:33:54.559969+00:00"}