{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ZGKLXHZDYUGU2PNBAGBSDHQKQC","short_pith_number":"pith:ZGKLXHZD","schema_version":"1.0","canonical_sha256":"c994bb9f23c50d4d3da10183219e0a80b4b23a0e50c3daba0c022ecb7c8b5886","source":{"kind":"arxiv","id":"2104.09789","version":1},"attestation_state":"computed","paper":{"title":"Does enhanced shape bias improve neural network robustness to common corruptions?","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chaithanya Kumar Mummadi, Jan Hendrik Metzen, Julien Vitay, Ranjitha Subramaniam, Robin Hutmacher, Volker Fischer","submitted_at":"2021-04-20T07:06:53Z","abstract_excerpt":"Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicates that CNNs trained on ImageNet are biased towards features that encode textures and that these alone are sufficient to generalize to unseen test data from the same distribution as the training data but often fail to generalize to out-of-distribution data. It has been shown that augmenting the training data with different image styles decreases this texture bias in favor of increased shape bias while at the same time"},"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":"2104.09789","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-04-20T07:06:53Z","cross_cats_sorted":[],"title_canon_sha256":"fae6ca304c4e41eba950fef59fb7f398fccf316f95fafb4da0df8007f4c0e275","abstract_canon_sha256":"219e433c9a6f71f206bc07bc6c864006cdbf7c8fa1ce29e43c27ba1db6909bb8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:33:36.919790Z","signature_b64":"XBd8qYK3W1STByFGh3ir/06FpWKZg4+x/5RSFj3eJTXkONg+UQzTws3TX4/XJ/wRjey7WtoScnE0ilUZQHLIBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c994bb9f23c50d4d3da10183219e0a80b4b23a0e50c3daba0c022ecb7c8b5886","last_reissued_at":"2026-07-05T02:33:36.919349Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:33:36.919349Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Does enhanced shape bias improve neural network robustness to common corruptions?","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chaithanya Kumar Mummadi, Jan Hendrik Metzen, Julien Vitay, Ranjitha Subramaniam, Robin Hutmacher, Volker Fischer","submitted_at":"2021-04-20T07:06:53Z","abstract_excerpt":"Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicates that CNNs trained on ImageNet are biased towards features that encode textures and that these alone are sufficient to generalize to unseen test data from the same distribution as the training data but often fail to generalize to out-of-distribution data. It has been shown that augmenting the training data with different image styles decreases this texture bias in favor of increased shape bias while at the same time"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.09789","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/2104.09789/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":"2104.09789","created_at":"2026-07-05T02:33:36.919411+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.09789v1","created_at":"2026-07-05T02:33:36.919411+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.09789","created_at":"2026-07-05T02:33:36.919411+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZGKLXHZDYUGU","created_at":"2026-07-05T02:33:36.919411+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZGKLXHZDYUGU2PNB","created_at":"2026-07-05T02:33:36.919411+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZGKLXHZD","created_at":"2026-07-05T02:33:36.919411+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.10442","citing_title":"Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZGKLXHZDYUGU2PNBAGBSDHQKQC","json":"https://pith.science/pith/ZGKLXHZDYUGU2PNBAGBSDHQKQC.json","graph_json":"https://pith.science/api/pith-number/ZGKLXHZDYUGU2PNBAGBSDHQKQC/graph.json","events_json":"https://pith.science/api/pith-number/ZGKLXHZDYUGU2PNBAGBSDHQKQC/events.json","paper":"https://pith.science/paper/ZGKLXHZD"},"agent_actions":{"view_html":"https://pith.science/pith/ZGKLXHZDYUGU2PNBAGBSDHQKQC","download_json":"https://pith.science/pith/ZGKLXHZDYUGU2PNBAGBSDHQKQC.json","view_paper":"https://pith.science/paper/ZGKLXHZD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.09789&json=true","fetch_graph":"https://pith.science/api/pith-number/ZGKLXHZDYUGU2PNBAGBSDHQKQC/graph.json","fetch_events":"https://pith.science/api/pith-number/ZGKLXHZDYUGU2PNBAGBSDHQKQC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZGKLXHZDYUGU2PNBAGBSDHQKQC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZGKLXHZDYUGU2PNBAGBSDHQKQC/action/storage_attestation","attest_author":"https://pith.science/pith/ZGKLXHZDYUGU2PNBAGBSDHQKQC/action/author_attestation","sign_citation":"https://pith.science/pith/ZGKLXHZDYUGU2PNBAGBSDHQKQC/action/citation_signature","submit_replication":"https://pith.science/pith/ZGKLXHZDYUGU2PNBAGBSDHQKQC/action/replication_record"}},"created_at":"2026-07-05T02:33:36.919411+00:00","updated_at":"2026-07-05T02:33:36.919411+00:00"}