{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7PPZVUTUPC3BUXBXDNCFNUBGPB","short_pith_number":"pith:7PPZVUTU","schema_version":"1.0","canonical_sha256":"fbdf9ad27478b61a5c371b4456d026787eb6585b88afa862114511d8f31af7a5","source":{"kind":"arxiv","id":"1901.07261","version":3},"attestation_state":"computed","paper":{"title":"Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Bo Zhang, Hailong Ma, Qingyuan Li, Ruijun Xu, Xiangxiang Chu","submitted_at":"2019-01-22T11:08:14Z","abstract_excerpt":"Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration capacity and the simplicity of models is still non-trivial. Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search. Specifically, we handle super-resolution with a multi-objective approach. We also propo"},"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":"1901.07261","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-22T11:08:14Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"558ac4bf374c4868cfb36ed72e682c781df95db6020a7571b34ce23221b8ae35","abstract_canon_sha256":"378563d95749e3c5d9929c249a3ac57ce55787f999e482b6aeb894101e8ab0d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:20:45.280395Z","signature_b64":"ubinKNU1kbmc5dmqkIA8t+qxVwOa5mPx1oJLsq1+ULUBn+l9pVMI9RiDaoU10rqLGdMU/5rOXonRAAz0VgLACA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbdf9ad27478b61a5c371b4456d026787eb6585b88afa862114511d8f31af7a5","last_reissued_at":"2026-07-05T01:20:45.279988Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:20:45.279988Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Bo Zhang, Hailong Ma, Qingyuan Li, Ruijun Xu, Xiangxiang Chu","submitted_at":"2019-01-22T11:08:14Z","abstract_excerpt":"Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration capacity and the simplicity of models is still non-trivial. Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search. Specifically, we handle super-resolution with a multi-objective approach. We also propo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.07261","kind":"arxiv","version":3},"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/1901.07261/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":"1901.07261","created_at":"2026-07-05T01:20:45.280047+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.07261v3","created_at":"2026-07-05T01:20:45.280047+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.07261","created_at":"2026-07-05T01:20:45.280047+00:00"},{"alias_kind":"pith_short_12","alias_value":"7PPZVUTUPC3B","created_at":"2026-07-05T01:20:45.280047+00:00"},{"alias_kind":"pith_short_16","alias_value":"7PPZVUTUPC3BUXBX","created_at":"2026-07-05T01:20:45.280047+00:00"},{"alias_kind":"pith_short_8","alias_value":"7PPZVUTU","created_at":"2026-07-05T01:20:45.280047+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.22351","citing_title":"QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7PPZVUTUPC3BUXBXDNCFNUBGPB","json":"https://pith.science/pith/7PPZVUTUPC3BUXBXDNCFNUBGPB.json","graph_json":"https://pith.science/api/pith-number/7PPZVUTUPC3BUXBXDNCFNUBGPB/graph.json","events_json":"https://pith.science/api/pith-number/7PPZVUTUPC3BUXBXDNCFNUBGPB/events.json","paper":"https://pith.science/paper/7PPZVUTU"},"agent_actions":{"view_html":"https://pith.science/pith/7PPZVUTUPC3BUXBXDNCFNUBGPB","download_json":"https://pith.science/pith/7PPZVUTUPC3BUXBXDNCFNUBGPB.json","view_paper":"https://pith.science/paper/7PPZVUTU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.07261&json=true","fetch_graph":"https://pith.science/api/pith-number/7PPZVUTUPC3BUXBXDNCFNUBGPB/graph.json","fetch_events":"https://pith.science/api/pith-number/7PPZVUTUPC3BUXBXDNCFNUBGPB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7PPZVUTUPC3BUXBXDNCFNUBGPB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7PPZVUTUPC3BUXBXDNCFNUBGPB/action/storage_attestation","attest_author":"https://pith.science/pith/7PPZVUTUPC3BUXBXDNCFNUBGPB/action/author_attestation","sign_citation":"https://pith.science/pith/7PPZVUTUPC3BUXBXDNCFNUBGPB/action/citation_signature","submit_replication":"https://pith.science/pith/7PPZVUTUPC3BUXBXDNCFNUBGPB/action/replication_record"}},"created_at":"2026-07-05T01:20:45.280047+00:00","updated_at":"2026-07-05T01:20:45.280047+00:00"}