{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7YODJBZ533KS7VPIL4UD7Q32MV","short_pith_number":"pith:7YODJBZ5","schema_version":"1.0","canonical_sha256":"fe1c34873dded52fd5e85f283fc37a6572d66b877d18ea8f9314fc68b5a51151","source":{"kind":"arxiv","id":"2205.05069","version":2},"attestation_state":"computed","paper":{"title":"Accelerating the Training of Video Super-Resolution Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CV","authors_text":"Lijian Lin, Xintao Wang, Ying Shan, Zhongang Qi","submitted_at":"2022-05-10T17:55:24Z","abstract_excerpt":"Despite that convolution neural networks (CNN) have recently demonstrated high-quality reconstruction for video super-resolution (VSR), efficiently training competitive VSR models remains a challenging problem. It usually takes an order of magnitude more time than training their counterpart image models, leading to long research cycles. Existing VSR methods typically train models with fixed spatial and temporal sizes from beginning to end. The fixed sizes are usually set to large values for good performance, resulting to slow training. However, is such a rigid training strategy necessary for V"},"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":"2205.05069","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-10T17:55:24Z","cross_cats_sorted":["cs.AI","cs.MM"],"title_canon_sha256":"fb6dd0a591d48286ceb553ec13e61b1a22759b0621d8761c3f27bbe629449d72","abstract_canon_sha256":"aa7159e2e190bc5641c1d28e95718a1ca607a7a980219fe81bf11ce7f62ecdaa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:23:56.176570Z","signature_b64":"NRUt0P8I/IV8yXYuGCtwci7Y0c4r29OgKMZNuV1/c4wTH8zzpWkPhzXOH73tGnIig+KdARp4+EZ92E6wGEDwAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe1c34873dded52fd5e85f283fc37a6572d66b877d18ea8f9314fc68b5a51151","last_reissued_at":"2026-07-05T04:23:56.176145Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:23:56.176145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating the Training of Video Super-Resolution Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CV","authors_text":"Lijian Lin, Xintao Wang, Ying Shan, Zhongang Qi","submitted_at":"2022-05-10T17:55:24Z","abstract_excerpt":"Despite that convolution neural networks (CNN) have recently demonstrated high-quality reconstruction for video super-resolution (VSR), efficiently training competitive VSR models remains a challenging problem. It usually takes an order of magnitude more time than training their counterpart image models, leading to long research cycles. Existing VSR methods typically train models with fixed spatial and temporal sizes from beginning to end. The fixed sizes are usually set to large values for good performance, resulting to slow training. However, is such a rigid training strategy necessary for V"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.05069","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/2205.05069/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":"2205.05069","created_at":"2026-07-05T04:23:56.176205+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.05069v2","created_at":"2026-07-05T04:23:56.176205+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.05069","created_at":"2026-07-05T04:23:56.176205+00:00"},{"alias_kind":"pith_short_12","alias_value":"7YODJBZ533KS","created_at":"2026-07-05T04:23:56.176205+00:00"},{"alias_kind":"pith_short_16","alias_value":"7YODJBZ533KS7VPI","created_at":"2026-07-05T04:23:56.176205+00:00"},{"alias_kind":"pith_short_8","alias_value":"7YODJBZ5","created_at":"2026-07-05T04:23:56.176205+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/7YODJBZ533KS7VPIL4UD7Q32MV","json":"https://pith.science/pith/7YODJBZ533KS7VPIL4UD7Q32MV.json","graph_json":"https://pith.science/api/pith-number/7YODJBZ533KS7VPIL4UD7Q32MV/graph.json","events_json":"https://pith.science/api/pith-number/7YODJBZ533KS7VPIL4UD7Q32MV/events.json","paper":"https://pith.science/paper/7YODJBZ5"},"agent_actions":{"view_html":"https://pith.science/pith/7YODJBZ533KS7VPIL4UD7Q32MV","download_json":"https://pith.science/pith/7YODJBZ533KS7VPIL4UD7Q32MV.json","view_paper":"https://pith.science/paper/7YODJBZ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.05069&json=true","fetch_graph":"https://pith.science/api/pith-number/7YODJBZ533KS7VPIL4UD7Q32MV/graph.json","fetch_events":"https://pith.science/api/pith-number/7YODJBZ533KS7VPIL4UD7Q32MV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7YODJBZ533KS7VPIL4UD7Q32MV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7YODJBZ533KS7VPIL4UD7Q32MV/action/storage_attestation","attest_author":"https://pith.science/pith/7YODJBZ533KS7VPIL4UD7Q32MV/action/author_attestation","sign_citation":"https://pith.science/pith/7YODJBZ533KS7VPIL4UD7Q32MV/action/citation_signature","submit_replication":"https://pith.science/pith/7YODJBZ533KS7VPIL4UD7Q32MV/action/replication_record"}},"created_at":"2026-07-05T04:23:56.176205+00:00","updated_at":"2026-07-05T04:23:56.176205+00:00"}