{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QITNUSPJRUI2KCLS3LSJDPMEE4","short_pith_number":"pith:QITNUSPJ","schema_version":"1.0","canonical_sha256":"8226da49e98d11a50972dae491bd84272e532ce58a0000c8691c407439924e49","source":{"kind":"arxiv","id":"2301.10575","version":2},"attestation_state":"computed","paper":{"title":"Trainable Loss Weights in Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Arash Chaichi Mellatshahi, Shohreh Kasaei","submitted_at":"2023-01-25T13:27:27Z","abstract_excerpt":"In recent years, limited research has discussed the loss function in the super-resolution process. The majority of those studies have only used perceptual similarity conventionally. This is while the development of appropriate loss can improve the quality of other methods as well. In this article, a new weighting method for pixel-wise loss is proposed. With the help of this method, it is possible to use trainable weights based on the general structure of the image and its perceptual features while maintaining the advantages of pixel-wise loss. Also, a criterion for comparing weights of loss is"},"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":"2301.10575","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-01-25T13:27:27Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"36fa94082e92b39c0823825315c49429c4c0651493225c8cb4238cff2687657c","abstract_canon_sha256":"74cbce81840e9e7a16185a4c23e86a721c4ae675d198629334bb3a5e5f59d0be"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:17:23.930819Z","signature_b64":"zj6O/NK8v47MM51rXcOVjT53XmX84fGLkGNRt5M74swRqmfIyaWwcS2EN+PQSSm2f7a4titXVXF6FtlQ0BxzCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8226da49e98d11a50972dae491bd84272e532ce58a0000c8691c407439924e49","last_reissued_at":"2026-07-05T07:17:23.930402Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:17:23.930402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trainable Loss Weights in Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Arash Chaichi Mellatshahi, Shohreh Kasaei","submitted_at":"2023-01-25T13:27:27Z","abstract_excerpt":"In recent years, limited research has discussed the loss function in the super-resolution process. The majority of those studies have only used perceptual similarity conventionally. This is while the development of appropriate loss can improve the quality of other methods as well. In this article, a new weighting method for pixel-wise loss is proposed. With the help of this method, it is possible to use trainable weights based on the general structure of the image and its perceptual features while maintaining the advantages of pixel-wise loss. Also, a criterion for comparing weights of loss is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.10575","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/2301.10575/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":"2301.10575","created_at":"2026-07-05T07:17:23.930466+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.10575v2","created_at":"2026-07-05T07:17:23.930466+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.10575","created_at":"2026-07-05T07:17:23.930466+00:00"},{"alias_kind":"pith_short_12","alias_value":"QITNUSPJRUI2","created_at":"2026-07-05T07:17:23.930466+00:00"},{"alias_kind":"pith_short_16","alias_value":"QITNUSPJRUI2KCLS","created_at":"2026-07-05T07:17:23.930466+00:00"},{"alias_kind":"pith_short_8","alias_value":"QITNUSPJ","created_at":"2026-07-05T07:17:23.930466+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/QITNUSPJRUI2KCLS3LSJDPMEE4","json":"https://pith.science/pith/QITNUSPJRUI2KCLS3LSJDPMEE4.json","graph_json":"https://pith.science/api/pith-number/QITNUSPJRUI2KCLS3LSJDPMEE4/graph.json","events_json":"https://pith.science/api/pith-number/QITNUSPJRUI2KCLS3LSJDPMEE4/events.json","paper":"https://pith.science/paper/QITNUSPJ"},"agent_actions":{"view_html":"https://pith.science/pith/QITNUSPJRUI2KCLS3LSJDPMEE4","download_json":"https://pith.science/pith/QITNUSPJRUI2KCLS3LSJDPMEE4.json","view_paper":"https://pith.science/paper/QITNUSPJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.10575&json=true","fetch_graph":"https://pith.science/api/pith-number/QITNUSPJRUI2KCLS3LSJDPMEE4/graph.json","fetch_events":"https://pith.science/api/pith-number/QITNUSPJRUI2KCLS3LSJDPMEE4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QITNUSPJRUI2KCLS3LSJDPMEE4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QITNUSPJRUI2KCLS3LSJDPMEE4/action/storage_attestation","attest_author":"https://pith.science/pith/QITNUSPJRUI2KCLS3LSJDPMEE4/action/author_attestation","sign_citation":"https://pith.science/pith/QITNUSPJRUI2KCLS3LSJDPMEE4/action/citation_signature","submit_replication":"https://pith.science/pith/QITNUSPJRUI2KCLS3LSJDPMEE4/action/replication_record"}},"created_at":"2026-07-05T07:17:23.930466+00:00","updated_at":"2026-07-05T07:17:23.930466+00:00"}