{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:2HNER4YVLQRAGG7JLP265OVBQA","short_pith_number":"pith:2HNER4YV","schema_version":"1.0","canonical_sha256":"d1da48f3155c22031be95bf5eebaa1802acd9cddb8c71d7ed05a04bd4b7659e5","source":{"kind":"arxiv","id":"2108.08910","version":2},"attestation_state":"computed","paper":{"title":"Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.NE"],"primary_cat":"eess.IV","authors_text":"Bin Ren, David Kaeli, Geng Yuan, Malith Jayaweera, Pu Zhao, Tianyun Zhang, Wei Niu, Xue Lin, Yanzhi Wang, Yifan Gong, Yushu Wu, Zheng Zhan","submitted_at":"2021-08-18T06:47:31Z","abstract_excerpt":"Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as mobile devices. To overcome the challenge and facilitate the real-time deployment of SISR tasks on mobile, we combine neural architecture search with pruning search and propose an automatic search framework that derives sparse super-resolution (SR) models with high image quality "},"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":"2108.08910","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-08-18T06:47:31Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.NE"],"title_canon_sha256":"afc72fcd78da4dd2486dfd421c3f3b554d085589f70a5f735e2eebaf8acb4943","abstract_canon_sha256":"8aaf82df9cf6161cef4b01cae33f6772a6e215fe4c64e60acb743a7db59ef499"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:41:14.949499Z","signature_b64":"VtVwLChVmbcwf86mxw4E63+Hq6Y49FgZVCEPbGyd+7F0msE5ofmH9vKpUo2yuzgFFMpG+LrypY60n1hvfLWACg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1da48f3155c22031be95bf5eebaa1802acd9cddb8c71d7ed05a04bd4b7659e5","last_reissued_at":"2026-07-05T05:41:14.949025Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:41:14.949025Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.NE"],"primary_cat":"eess.IV","authors_text":"Bin Ren, David Kaeli, Geng Yuan, Malith Jayaweera, Pu Zhao, Tianyun Zhang, Wei Niu, Xue Lin, Yanzhi Wang, Yifan Gong, Yushu Wu, Zheng Zhan","submitted_at":"2021-08-18T06:47:31Z","abstract_excerpt":"Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as mobile devices. To overcome the challenge and facilitate the real-time deployment of SISR tasks on mobile, we combine neural architecture search with pruning search and propose an automatic search framework that derives sparse super-resolution (SR) models with high image quality "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.08910","kind":"arxiv","version":2},"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/2108.08910/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":"2108.08910","created_at":"2026-07-05T05:41:14.949080+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.08910v2","created_at":"2026-07-05T05:41:14.949080+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.08910","created_at":"2026-07-05T05:41:14.949080+00:00"},{"alias_kind":"pith_short_12","alias_value":"2HNER4YVLQRA","created_at":"2026-07-05T05:41:14.949080+00:00"},{"alias_kind":"pith_short_16","alias_value":"2HNER4YVLQRAGG7J","created_at":"2026-07-05T05:41:14.949080+00:00"},{"alias_kind":"pith_short_8","alias_value":"2HNER4YV","created_at":"2026-07-05T05:41:14.949080+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/2HNER4YVLQRAGG7JLP265OVBQA","json":"https://pith.science/pith/2HNER4YVLQRAGG7JLP265OVBQA.json","graph_json":"https://pith.science/api/pith-number/2HNER4YVLQRAGG7JLP265OVBQA/graph.json","events_json":"https://pith.science/api/pith-number/2HNER4YVLQRAGG7JLP265OVBQA/events.json","paper":"https://pith.science/paper/2HNER4YV"},"agent_actions":{"view_html":"https://pith.science/pith/2HNER4YVLQRAGG7JLP265OVBQA","download_json":"https://pith.science/pith/2HNER4YVLQRAGG7JLP265OVBQA.json","view_paper":"https://pith.science/paper/2HNER4YV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.08910&json=true","fetch_graph":"https://pith.science/api/pith-number/2HNER4YVLQRAGG7JLP265OVBQA/graph.json","fetch_events":"https://pith.science/api/pith-number/2HNER4YVLQRAGG7JLP265OVBQA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2HNER4YVLQRAGG7JLP265OVBQA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2HNER4YVLQRAGG7JLP265OVBQA/action/storage_attestation","attest_author":"https://pith.science/pith/2HNER4YVLQRAGG7JLP265OVBQA/action/author_attestation","sign_citation":"https://pith.science/pith/2HNER4YVLQRAGG7JLP265OVBQA/action/citation_signature","submit_replication":"https://pith.science/pith/2HNER4YVLQRAGG7JLP265OVBQA/action/replication_record"}},"created_at":"2026-07-05T05:41:14.949080+00:00","updated_at":"2026-07-05T05:41:14.949080+00:00"}