{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:MVAET3LPO3V7M6IF2VW7S7BVGH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b1938500c125e5003ec7133222f9ad597a9dbf6828752a92f9686e0476160c4d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-08T18:05:58Z","title_canon_sha256":"b20bc7919bb947afd3d2e2144a21fb84e63ac064a8377fa39485c7ddfd865fd2"},"schema_version":"1.0","source":{"id":"2207.04075","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.04075","created_at":"2026-07-05T04:38:55Z"},{"alias_kind":"arxiv_version","alias_value":"2207.04075v1","created_at":"2026-07-05T04:38:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.04075","created_at":"2026-07-05T04:38:55Z"},{"alias_kind":"pith_short_12","alias_value":"MVAET3LPO3V7","created_at":"2026-07-05T04:38:55Z"},{"alias_kind":"pith_short_16","alias_value":"MVAET3LPO3V7M6IF","created_at":"2026-07-05T04:38:55Z"},{"alias_kind":"pith_short_8","alias_value":"MVAET3LP","created_at":"2026-07-05T04:38:55Z"}],"graph_snapshots":[{"event_id":"sha256:59ad6101742e0863f6196590adaf091e483cdeb4b2e3872357f560c9514ddbcd","target":"graph","created_at":"2026-07-05T04:38:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2207.04075/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Improving the accuracy of deep neural networks (DNNs) on out-of-distribution (OOD) data is critical to an acceptance of deep learning (DL) in real world applications. It has been observed that accuracies on in-distribution (ID) versus OOD data follow a linear trend and models that outperform this baseline are exceptionally rare (and referred to as \"effectively robust\"). Recently, some promising approaches have been developed to improve OOD robustness: model pruning, data augmentation, and ensembling or zero-shot evaluating large pretrained models. However, there still is no clear understanding","authors_text":"Bhavya Kailkhura, Brian R. Bartoldson, James Diffenderfer, Peer-Timo Bremer, Sara Fridovich-Keil","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-08T18:05:58Z","title":"Models Out of Line: A Fourier Lens on Distribution Shift Robustness"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.04075","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d2007cef005818299adbdb08b26f49dd9da05df41eb35fd610667a2d9353f0e3","target":"record","created_at":"2026-07-05T04:38:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b1938500c125e5003ec7133222f9ad597a9dbf6828752a92f9686e0476160c4d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-08T18:05:58Z","title_canon_sha256":"b20bc7919bb947afd3d2e2144a21fb84e63ac064a8377fa39485c7ddfd865fd2"},"schema_version":"1.0","source":{"id":"2207.04075","kind":"arxiv","version":1}},"canonical_sha256":"654049ed6f76ebf67905d56df97c3531ebc8b10858119d0a2e641d3e916373da","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"654049ed6f76ebf67905d56df97c3531ebc8b10858119d0a2e641d3e916373da","first_computed_at":"2026-07-05T04:38:55.156728Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:38:55.156728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2tiLCAsGu4ktXMRdAdiMWi8HE1thqFfcK52s0uSe4DAYhk2OsQy3o6/Z24UqrWi3xkNAiyKCuDCakPTZ3GxJCw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:38:55.157172Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.04075","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d2007cef005818299adbdb08b26f49dd9da05df41eb35fd610667a2d9353f0e3","sha256:59ad6101742e0863f6196590adaf091e483cdeb4b2e3872357f560c9514ddbcd"],"state_sha256":"03749fd434a0435137a91030178708b1a168e0ecbdf3116a33bb68506287e4f4"}