{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZEZHSVYNGERKQNXMD3EW2D5DS5","short_pith_number":"pith:ZEZHSVYN","schema_version":"1.0","canonical_sha256":"c93279570d3122a836ec1ec96d0fa397584f43ec0160930264a0e32b317a0c86","source":{"kind":"arxiv","id":"2402.19401","version":1},"attestation_state":"computed","paper":{"title":"Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Boyue Caroline Hu, Huakun Shen, Krzysztof Czarnecki, Lina Marsso, Marsha Chechik","submitted_at":"2024-02-29T18:00:27Z","abstract_excerpt":"While Neural Networks (NNs) have surpassed human accuracy in image classification on ImageNet, they often lack robustness against image corruption, i.e., corruption robustness. Yet such robustness is seemingly effortless for human perception. In this paper, we propose visually-continuous corruption robustness (VCR) -- an extension of corruption robustness to allow assessing it over the wide and continuous range of changes that correspond to the human perceptive quality (i.e., from the original image to the full distortion of all perceived visual information), along with two novel human-aware m"},"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":"2402.19401","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-29T18:00:27Z","cross_cats_sorted":[],"title_canon_sha256":"012afc1d9cbd3f61470c604bca0fb86b3a9acd6ed2b29360b4f86f627065af05","abstract_canon_sha256":"a9db6de772b9ea965b98e217d91504a6568ec19eb6e5e74475bf605e2039c865"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:44.772527Z","signature_b64":"DwKn/x3e4sM8ztq/IVRi+xtl4ckOJzXcayFt938x32g5txUq2IbuKapAG9r7J0fWe54c3AuA4WgyBHpI+fMRBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c93279570d3122a836ec1ec96d0fa397584f43ec0160930264a0e32b317a0c86","last_reissued_at":"2026-07-05T07:50:44.772038Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:44.772038Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Boyue Caroline Hu, Huakun Shen, Krzysztof Czarnecki, Lina Marsso, Marsha Chechik","submitted_at":"2024-02-29T18:00:27Z","abstract_excerpt":"While Neural Networks (NNs) have surpassed human accuracy in image classification on ImageNet, they often lack robustness against image corruption, i.e., corruption robustness. Yet such robustness is seemingly effortless for human perception. In this paper, we propose visually-continuous corruption robustness (VCR) -- an extension of corruption robustness to allow assessing it over the wide and continuous range of changes that correspond to the human perceptive quality (i.e., from the original image to the full distortion of all perceived visual information), along with two novel human-aware m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.19401","kind":"arxiv","version":1},"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/2402.19401/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":"2402.19401","created_at":"2026-07-05T07:50:44.772090+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.19401v1","created_at":"2026-07-05T07:50:44.772090+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.19401","created_at":"2026-07-05T07:50:44.772090+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZEZHSVYNGERK","created_at":"2026-07-05T07:50:44.772090+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZEZHSVYNGERKQNXM","created_at":"2026-07-05T07:50:44.772090+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZEZHSVYN","created_at":"2026-07-05T07:50:44.772090+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.10289","citing_title":"SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks","ref_index":64,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZEZHSVYNGERKQNXMD3EW2D5DS5","json":"https://pith.science/pith/ZEZHSVYNGERKQNXMD3EW2D5DS5.json","graph_json":"https://pith.science/api/pith-number/ZEZHSVYNGERKQNXMD3EW2D5DS5/graph.json","events_json":"https://pith.science/api/pith-number/ZEZHSVYNGERKQNXMD3EW2D5DS5/events.json","paper":"https://pith.science/paper/ZEZHSVYN"},"agent_actions":{"view_html":"https://pith.science/pith/ZEZHSVYNGERKQNXMD3EW2D5DS5","download_json":"https://pith.science/pith/ZEZHSVYNGERKQNXMD3EW2D5DS5.json","view_paper":"https://pith.science/paper/ZEZHSVYN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.19401&json=true","fetch_graph":"https://pith.science/api/pith-number/ZEZHSVYNGERKQNXMD3EW2D5DS5/graph.json","fetch_events":"https://pith.science/api/pith-number/ZEZHSVYNGERKQNXMD3EW2D5DS5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZEZHSVYNGERKQNXMD3EW2D5DS5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZEZHSVYNGERKQNXMD3EW2D5DS5/action/storage_attestation","attest_author":"https://pith.science/pith/ZEZHSVYNGERKQNXMD3EW2D5DS5/action/author_attestation","sign_citation":"https://pith.science/pith/ZEZHSVYNGERKQNXMD3EW2D5DS5/action/citation_signature","submit_replication":"https://pith.science/pith/ZEZHSVYNGERKQNXMD3EW2D5DS5/action/replication_record"}},"created_at":"2026-07-05T07:50:44.772090+00:00","updated_at":"2026-07-05T07:50:44.772090+00:00"}