{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:76QRACQFXIJZJM2MHZ6PPWT5W2","short_pith_number":"pith:76QRACQF","canonical_record":{"source":{"id":"2312.15288","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-23T16:05:47Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e754310acc469ac8bc1623e5c3de670e55abeedd17ae64263970f64b32764aee","abstract_canon_sha256":"73534a13af617f077120e947c2f60ac7bb4326ab3b1bbb3490fd93a29ded7967"},"schema_version":"1.0"},"canonical_sha256":"ffa1100a05ba1394b34c3e7cf7da7db699647c38af677b6434c84627f8b3734a","source":{"kind":"arxiv","id":"2312.15288","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.15288","created_at":"2026-07-05T08:05:07Z"},{"alias_kind":"arxiv_version","alias_value":"2312.15288v2","created_at":"2026-07-05T08:05:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.15288","created_at":"2026-07-05T08:05:07Z"},{"alias_kind":"pith_short_12","alias_value":"76QRACQFXIJZ","created_at":"2026-07-05T08:05:07Z"},{"alias_kind":"pith_short_16","alias_value":"76QRACQFXIJZJM2M","created_at":"2026-07-05T08:05:07Z"},{"alias_kind":"pith_short_8","alias_value":"76QRACQF","created_at":"2026-07-05T08:05:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:76QRACQFXIJZJM2MHZ6PPWT5W2","target":"record","payload":{"canonical_record":{"source":{"id":"2312.15288","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-23T16:05:47Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e754310acc469ac8bc1623e5c3de670e55abeedd17ae64263970f64b32764aee","abstract_canon_sha256":"73534a13af617f077120e947c2f60ac7bb4326ab3b1bbb3490fd93a29ded7967"},"schema_version":"1.0"},"canonical_sha256":"ffa1100a05ba1394b34c3e7cf7da7db699647c38af677b6434c84627f8b3734a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:05:07.605235Z","signature_b64":"ZZCxfbKmblFfQrY+S7FVGSwQlaP9rQNEVno/MHczJL5VYigJez/h8veiQYgU7xU/GrOKh2SB2re49Nh2Pu70Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ffa1100a05ba1394b34c3e7cf7da7db699647c38af677b6434c84627f8b3734a","last_reissued_at":"2026-07-05T08:05:07.604825Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:05:07.604825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.15288","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:05:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9gt2JRhjZuGHD7cpIwqGIpnPIHLKjpqqrp9QPfKSgcrTPF/kZctDcr8XXeHH4oYWoIczXE2bfjc+Bs1sIjxDBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:50:09.520160Z"},"content_sha256":"2b694a948f23707a1309bd38891bf9c778174e1bd895cdcfd7fc696964019a74","schema_version":"1.0","event_id":"sha256:2b694a948f23707a1309bd38891bf9c778174e1bd895cdcfd7fc696964019a74"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:76QRACQFXIJZJM2MHZ6PPWT5W2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Understanding normalization in contrastive representation learning and out-of-distribution detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Jaehyun Ahn, Tai Le-Gia","submitted_at":"2023-12-23T16:05:47Z","abstract_excerpt":"Contrastive representation learning has emerged as an outstanding approach for anomaly detection. In this work, we explore the $\\ell_2$-norm of contrastive features and its applications in out-of-distribution detection. We propose a simple method based on contrastive learning, which incorporates out-of-distribution data by discriminating against normal samples in the contrastive layer space. Our approach can be applied flexibly as an outlier exposure (OE) approach, where the out-of-distribution data is a huge collective of random images, or as a fully self-supervised learning approach, where t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15288","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/2312.15288/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:05:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rMQR/eq8zq5Zqb9MkvT+wCF4Ozbk40hqCkuh/ppp7c5LXC4WFPRDnz0wuiFnREjsU93upPlfRO/BiA+fP8tGCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:50:09.520786Z"},"content_sha256":"304cce83047627ef715cf094187ae2e642b764e7ac7f6de002ab9a90f4c5bf76","schema_version":"1.0","event_id":"sha256:304cce83047627ef715cf094187ae2e642b764e7ac7f6de002ab9a90f4c5bf76"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/76QRACQFXIJZJM2MHZ6PPWT5W2/bundle.json","state_url":"https://pith.science/pith/76QRACQFXIJZJM2MHZ6PPWT5W2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/76QRACQFXIJZJM2MHZ6PPWT5W2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T04:50:09Z","links":{"resolver":"https://pith.science/pith/76QRACQFXIJZJM2MHZ6PPWT5W2","bundle":"https://pith.science/pith/76QRACQFXIJZJM2MHZ6PPWT5W2/bundle.json","state":"https://pith.science/pith/76QRACQFXIJZJM2MHZ6PPWT5W2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/76QRACQFXIJZJM2MHZ6PPWT5W2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:76QRACQFXIJZJM2MHZ6PPWT5W2","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":"73534a13af617f077120e947c2f60ac7bb4326ab3b1bbb3490fd93a29ded7967","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-23T16:05:47Z","title_canon_sha256":"e754310acc469ac8bc1623e5c3de670e55abeedd17ae64263970f64b32764aee"},"schema_version":"1.0","source":{"id":"2312.15288","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.15288","created_at":"2026-07-05T08:05:07Z"},{"alias_kind":"arxiv_version","alias_value":"2312.15288v2","created_at":"2026-07-05T08:05:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.15288","created_at":"2026-07-05T08:05:07Z"},{"alias_kind":"pith_short_12","alias_value":"76QRACQFXIJZ","created_at":"2026-07-05T08:05:07Z"},{"alias_kind":"pith_short_16","alias_value":"76QRACQFXIJZJM2M","created_at":"2026-07-05T08:05:07Z"},{"alias_kind":"pith_short_8","alias_value":"76QRACQF","created_at":"2026-07-05T08:05:07Z"}],"graph_snapshots":[{"event_id":"sha256:304cce83047627ef715cf094187ae2e642b764e7ac7f6de002ab9a90f4c5bf76","target":"graph","created_at":"2026-07-05T08:05:07Z","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/2312.15288/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Contrastive representation learning has emerged as an outstanding approach for anomaly detection. In this work, we explore the $\\ell_2$-norm of contrastive features and its applications in out-of-distribution detection. We propose a simple method based on contrastive learning, which incorporates out-of-distribution data by discriminating against normal samples in the contrastive layer space. Our approach can be applied flexibly as an outlier exposure (OE) approach, where the out-of-distribution data is a huge collective of random images, or as a fully self-supervised learning approach, where t","authors_text":"Jaehyun Ahn, Tai Le-Gia","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-23T16:05:47Z","title":"Understanding normalization in contrastive representation learning and out-of-distribution detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15288","kind":"arxiv","version":2},"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":"sha256:2b694a948f23707a1309bd38891bf9c778174e1bd895cdcfd7fc696964019a74","target":"record","created_at":"2026-07-05T08:05:07Z","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":"73534a13af617f077120e947c2f60ac7bb4326ab3b1bbb3490fd93a29ded7967","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-23T16:05:47Z","title_canon_sha256":"e754310acc469ac8bc1623e5c3de670e55abeedd17ae64263970f64b32764aee"},"schema_version":"1.0","source":{"id":"2312.15288","kind":"arxiv","version":2}},"canonical_sha256":"ffa1100a05ba1394b34c3e7cf7da7db699647c38af677b6434c84627f8b3734a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ffa1100a05ba1394b34c3e7cf7da7db699647c38af677b6434c84627f8b3734a","first_computed_at":"2026-07-05T08:05:07.604825Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:05:07.604825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZZCxfbKmblFfQrY+S7FVGSwQlaP9rQNEVno/MHczJL5VYigJez/h8veiQYgU7xU/GrOKh2SB2re49Nh2Pu70Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:05:07.605235Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.15288","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b694a948f23707a1309bd38891bf9c778174e1bd895cdcfd7fc696964019a74","sha256:304cce83047627ef715cf094187ae2e642b764e7ac7f6de002ab9a90f4c5bf76"],"state_sha256":"5a9a9a9a979645b21eb89b71a013b5605d8f1c50049924ba522c9426055b43cf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"olrtZ/RehbdMl205yeHMsQOfNrmyPdLC0kg4w4tPzMTdg8rC82sBJZBxyakQzSZQywBjoPDGPV9PRohqAdPaAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T04:50:09.524864Z","bundle_sha256":"585c396f1b9bc3ebe65b7769affe1b035b1e16f88efecf3962f84fd6a00fd4f2"}}