{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:5ILRUIHTGYXV2DRSF3GDZHFM6F","short_pith_number":"pith:5ILRUIHT","schema_version":"1.0","canonical_sha256":"ea171a20f3362f5d0e322ecc3c9cacf15f64c5b3efb1c643a9fb67e395dd95cd","source":{"kind":"arxiv","id":"2209.00364","version":1},"attestation_state":"computed","paper":{"title":"Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nicolas Jourdan, Nils G\\\"ahlert, Yannik Blei","submitted_at":"2022-09-01T11:14:57Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) are nowadays often employed in vision-based perception stacks for safetycritical applications such as autonomous driving or Unmanned Aerial Vehicles (UAVs). Due to the safety requirements in those use cases, it is important to know the limitations of the CNN and, thus, to detect Out-of-Distribution (OOD) samples. In this work, we present an approach to enable OOD detection for 2D object detection by employing the margin entropy (ME) loss. The proposed method is easy to implement and can be applied to most existing object detection architectures. In addition"},"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":"2209.00364","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-01T11:14:57Z","cross_cats_sorted":[],"title_canon_sha256":"2e6b64ff99bdf591406ebc35f5571ba2bf81ce7960736d0282f8407ba420a922","abstract_canon_sha256":"10d4ebf8c7374e70e0a869c6f1305131479396f6269f17b3fedf15a3185436f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:53:48.772362Z","signature_b64":"t0+AAaYd0XSNy138BQuliMpO5GFC2aaa9TdKz+/UoALTsOn0nt6JGThxgepk0+uBostEZeHuTKjjI0/4aO80Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea171a20f3362f5d0e322ecc3c9cacf15f64c5b3efb1c643a9fb67e395dd95cd","last_reissued_at":"2026-07-05T04:53:48.771879Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:53:48.771879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nicolas Jourdan, Nils G\\\"ahlert, Yannik Blei","submitted_at":"2022-09-01T11:14:57Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) are nowadays often employed in vision-based perception stacks for safetycritical applications such as autonomous driving or Unmanned Aerial Vehicles (UAVs). Due to the safety requirements in those use cases, it is important to know the limitations of the CNN and, thus, to detect Out-of-Distribution (OOD) samples. In this work, we present an approach to enable OOD detection for 2D object detection by employing the margin entropy (ME) loss. The proposed method is easy to implement and can be applied to most existing object detection architectures. In addition"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.00364","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/2209.00364/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":"2209.00364","created_at":"2026-07-05T04:53:48.771945+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.00364v1","created_at":"2026-07-05T04:53:48.771945+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.00364","created_at":"2026-07-05T04:53:48.771945+00:00"},{"alias_kind":"pith_short_12","alias_value":"5ILRUIHTGYXV","created_at":"2026-07-05T04:53:48.771945+00:00"},{"alias_kind":"pith_short_16","alias_value":"5ILRUIHTGYXV2DRS","created_at":"2026-07-05T04:53:48.771945+00:00"},{"alias_kind":"pith_short_8","alias_value":"5ILRUIHT","created_at":"2026-07-05T04:53:48.771945+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/5ILRUIHTGYXV2DRSF3GDZHFM6F","json":"https://pith.science/pith/5ILRUIHTGYXV2DRSF3GDZHFM6F.json","graph_json":"https://pith.science/api/pith-number/5ILRUIHTGYXV2DRSF3GDZHFM6F/graph.json","events_json":"https://pith.science/api/pith-number/5ILRUIHTGYXV2DRSF3GDZHFM6F/events.json","paper":"https://pith.science/paper/5ILRUIHT"},"agent_actions":{"view_html":"https://pith.science/pith/5ILRUIHTGYXV2DRSF3GDZHFM6F","download_json":"https://pith.science/pith/5ILRUIHTGYXV2DRSF3GDZHFM6F.json","view_paper":"https://pith.science/paper/5ILRUIHT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.00364&json=true","fetch_graph":"https://pith.science/api/pith-number/5ILRUIHTGYXV2DRSF3GDZHFM6F/graph.json","fetch_events":"https://pith.science/api/pith-number/5ILRUIHTGYXV2DRSF3GDZHFM6F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5ILRUIHTGYXV2DRSF3GDZHFM6F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5ILRUIHTGYXV2DRSF3GDZHFM6F/action/storage_attestation","attest_author":"https://pith.science/pith/5ILRUIHTGYXV2DRSF3GDZHFM6F/action/author_attestation","sign_citation":"https://pith.science/pith/5ILRUIHTGYXV2DRSF3GDZHFM6F/action/citation_signature","submit_replication":"https://pith.science/pith/5ILRUIHTGYXV2DRSF3GDZHFM6F/action/replication_record"}},"created_at":"2026-07-05T04:53:48.771945+00:00","updated_at":"2026-07-05T04:53:48.771945+00:00"}