{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GKEAECTPAYVJULP2GP3PPN626G","short_pith_number":"pith:GKEAECTP","schema_version":"1.0","canonical_sha256":"3288020a6f062a9a2dfa33f6f7b7daf1ba58cd5d2834557c3954438319503745","source":{"kind":"arxiv","id":"1812.01458","version":1},"attestation_state":"computed","paper":{"title":"Deep Inception Generative Network for Cognitive Image Inpainting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangyao Li, Qiaochuan Chen, Qingguo Xiao","submitted_at":"2018-12-01T03:20:47Z","abstract_excerpt":"Recent advances in deep learning have shown exciting promise in filling large holes and lead to another orientation for image inpainting. However, existing learning-based methods often create artifacts and fallacious textures because of insufficient cognition understanding. Previous generative networks are limited with single receptive type and give up pooling in consideration of detail sharpness. Human cognition is constant regardless of the target attribute. As multiple receptive fields improve the ability of abstract image characterization and pooling can keep feature invariant, specificall"},"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":"1812.01458","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-01T03:20:47Z","cross_cats_sorted":[],"title_canon_sha256":"3f7ecd45f807fb1a5ba07fd83b0761ec9110990109c80b8611ba2adf8cdb96fb","abstract_canon_sha256":"44b17300abd8f29dbc48421500d4040f5f06b9cca8a64254db8d740561c0c835"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:12.408874Z","signature_b64":"LCuWB6clFGSHDpSJdQ17AV841iHkynex7/9bwiBDyoc+Kc+kHpy+4Ix6nrc1TUHumxYPIztwdZLFo4+gKeTcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3288020a6f062a9a2dfa33f6f7b7daf1ba58cd5d2834557c3954438319503745","last_reissued_at":"2026-05-17T23:59:12.408139Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:12.408139Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Inception Generative Network for Cognitive Image Inpainting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangyao Li, Qiaochuan Chen, Qingguo Xiao","submitted_at":"2018-12-01T03:20:47Z","abstract_excerpt":"Recent advances in deep learning have shown exciting promise in filling large holes and lead to another orientation for image inpainting. However, existing learning-based methods often create artifacts and fallacious textures because of insufficient cognition understanding. Previous generative networks are limited with single receptive type and give up pooling in consideration of detail sharpness. Human cognition is constant regardless of the target attribute. As multiple receptive fields improve the ability of abstract image characterization and pooling can keep feature invariant, specificall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01458","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":"1812.01458","created_at":"2026-05-17T23:59:12.408269+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.01458v1","created_at":"2026-05-17T23:59:12.408269+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.01458","created_at":"2026-05-17T23:59:12.408269+00:00"},{"alias_kind":"pith_short_12","alias_value":"GKEAECTPAYVJ","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GKEAECTPAYVJULP2","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GKEAECTP","created_at":"2026-05-18T12:32:25.280505+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/GKEAECTPAYVJULP2GP3PPN626G","json":"https://pith.science/pith/GKEAECTPAYVJULP2GP3PPN626G.json","graph_json":"https://pith.science/api/pith-number/GKEAECTPAYVJULP2GP3PPN626G/graph.json","events_json":"https://pith.science/api/pith-number/GKEAECTPAYVJULP2GP3PPN626G/events.json","paper":"https://pith.science/paper/GKEAECTP"},"agent_actions":{"view_html":"https://pith.science/pith/GKEAECTPAYVJULP2GP3PPN626G","download_json":"https://pith.science/pith/GKEAECTPAYVJULP2GP3PPN626G.json","view_paper":"https://pith.science/paper/GKEAECTP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.01458&json=true","fetch_graph":"https://pith.science/api/pith-number/GKEAECTPAYVJULP2GP3PPN626G/graph.json","fetch_events":"https://pith.science/api/pith-number/GKEAECTPAYVJULP2GP3PPN626G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GKEAECTPAYVJULP2GP3PPN626G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GKEAECTPAYVJULP2GP3PPN626G/action/storage_attestation","attest_author":"https://pith.science/pith/GKEAECTPAYVJULP2GP3PPN626G/action/author_attestation","sign_citation":"https://pith.science/pith/GKEAECTPAYVJULP2GP3PPN626G/action/citation_signature","submit_replication":"https://pith.science/pith/GKEAECTPAYVJULP2GP3PPN626G/action/replication_record"}},"created_at":"2026-05-17T23:59:12.408269+00:00","updated_at":"2026-05-17T23:59:12.408269+00:00"}