{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:DJVJTPGFK3R47O4WICTZB2CCN2","short_pith_number":"pith:DJVJTPGF","schema_version":"1.0","canonical_sha256":"1a6a99bcc556e3cfbb9640a790e8426e97fcac0a161100a854c0ed560c18323f","source":{"kind":"arxiv","id":"2203.13558","version":2},"attestation_state":"computed","paper":{"title":"Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jes\\'us Malo (Image Processing Lab., Pablo Hern\\'andez-C\\'amara, Universitat de Val\\`encia), Valero Laparra","submitted_at":"2022-03-25T10:26:39Z","abstract_excerpt":"One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensory neuroscience is divisive normalization, and it has appealing effects on image manifolds. In this work we show that including divisive normalization in current deep networks makes them more invariant to non-informative changes in the images. In particular, we focus on U-Net architectures for image segmentation. Experiments show that the inclusion of divisive normalization in the U-Net architecture leads to better se"},"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":"2203.13558","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-25T10:26:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0372d9304538587a651b937c765882dd364f05589f815bf512a5d5fbb4cae0b4","abstract_canon_sha256":"44a7c0361cb65461d47ff352a401291c78a08921c1b46232856ece914e602d70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:14:33.592796Z","signature_b64":"KaOPmnAEq1vv3Dlw9y01A8iNfvNf5udXDo2A4hNA7Jmuvvhr3duP+tPpau6+4gz9jZ4osfU493tQ8EOxkWImDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a6a99bcc556e3cfbb9640a790e8426e97fcac0a161100a854c0ed560c18323f","last_reissued_at":"2026-07-05T05:14:33.592099Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:14:33.592099Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jes\\'us Malo (Image Processing Lab., Pablo Hern\\'andez-C\\'amara, Universitat de Val\\`encia), Valero Laparra","submitted_at":"2022-03-25T10:26:39Z","abstract_excerpt":"One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensory neuroscience is divisive normalization, and it has appealing effects on image manifolds. In this work we show that including divisive normalization in current deep networks makes them more invariant to non-informative changes in the images. In particular, we focus on U-Net architectures for image segmentation. Experiments show that the inclusion of divisive normalization in the U-Net architecture leads to better se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.13558","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/2203.13558/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":"2203.13558","created_at":"2026-07-05T05:14:33.592154+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.13558v2","created_at":"2026-07-05T05:14:33.592154+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.13558","created_at":"2026-07-05T05:14:33.592154+00:00"},{"alias_kind":"pith_short_12","alias_value":"DJVJTPGFK3R4","created_at":"2026-07-05T05:14:33.592154+00:00"},{"alias_kind":"pith_short_16","alias_value":"DJVJTPGFK3R47O4W","created_at":"2026-07-05T05:14:33.592154+00:00"},{"alias_kind":"pith_short_8","alias_value":"DJVJTPGF","created_at":"2026-07-05T05:14:33.592154+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/DJVJTPGFK3R47O4WICTZB2CCN2","json":"https://pith.science/pith/DJVJTPGFK3R47O4WICTZB2CCN2.json","graph_json":"https://pith.science/api/pith-number/DJVJTPGFK3R47O4WICTZB2CCN2/graph.json","events_json":"https://pith.science/api/pith-number/DJVJTPGFK3R47O4WICTZB2CCN2/events.json","paper":"https://pith.science/paper/DJVJTPGF"},"agent_actions":{"view_html":"https://pith.science/pith/DJVJTPGFK3R47O4WICTZB2CCN2","download_json":"https://pith.science/pith/DJVJTPGFK3R47O4WICTZB2CCN2.json","view_paper":"https://pith.science/paper/DJVJTPGF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.13558&json=true","fetch_graph":"https://pith.science/api/pith-number/DJVJTPGFK3R47O4WICTZB2CCN2/graph.json","fetch_events":"https://pith.science/api/pith-number/DJVJTPGFK3R47O4WICTZB2CCN2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DJVJTPGFK3R47O4WICTZB2CCN2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DJVJTPGFK3R47O4WICTZB2CCN2/action/storage_attestation","attest_author":"https://pith.science/pith/DJVJTPGFK3R47O4WICTZB2CCN2/action/author_attestation","sign_citation":"https://pith.science/pith/DJVJTPGFK3R47O4WICTZB2CCN2/action/citation_signature","submit_replication":"https://pith.science/pith/DJVJTPGFK3R47O4WICTZB2CCN2/action/replication_record"}},"created_at":"2026-07-05T05:14:33.592154+00:00","updated_at":"2026-07-05T05:14:33.592154+00:00"}