{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:GIRJPA6EZDONXAK5FX3CS52DAX","short_pith_number":"pith:GIRJPA6E","canonical_record":{"source":{"id":"2109.09416","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-20T10:31:50Z","cross_cats_sorted":[],"title_canon_sha256":"6cd5ad1c93da74e25bcd93fc238b739f948db9c117468c4a56300221c53a0668","abstract_canon_sha256":"aa1f29a17fccd7223b4e414615b780f67cb0901bfcacdd8aa389bbcdc899ca62"},"schema_version":"1.0"},"canonical_sha256":"32229783c4c8dcdb815d2df629774305ebbbc4a9aa2456283a01c4abb47e9f2f","source":{"kind":"arxiv","id":"2109.09416","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.09416","created_at":"2026-07-05T04:06:45Z"},{"alias_kind":"arxiv_version","alias_value":"2109.09416v4","created_at":"2026-07-05T04:06:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.09416","created_at":"2026-07-05T04:06:45Z"},{"alias_kind":"pith_short_12","alias_value":"GIRJPA6EZDON","created_at":"2026-07-05T04:06:45Z"},{"alias_kind":"pith_short_16","alias_value":"GIRJPA6EZDONXAK5","created_at":"2026-07-05T04:06:45Z"},{"alias_kind":"pith_short_8","alias_value":"GIRJPA6E","created_at":"2026-07-05T04:06:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:GIRJPA6EZDONXAK5FX3CS52DAX","target":"record","payload":{"canonical_record":{"source":{"id":"2109.09416","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-20T10:31:50Z","cross_cats_sorted":[],"title_canon_sha256":"6cd5ad1c93da74e25bcd93fc238b739f948db9c117468c4a56300221c53a0668","abstract_canon_sha256":"aa1f29a17fccd7223b4e414615b780f67cb0901bfcacdd8aa389bbcdc899ca62"},"schema_version":"1.0"},"canonical_sha256":"32229783c4c8dcdb815d2df629774305ebbbc4a9aa2456283a01c4abb47e9f2f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:06:45.173251Z","signature_b64":"NjMX420wZVvctd5Pl0ek1GrGYKO8V4cBFGqo7iI/hsJKnpb6qd16OBUcQlhAQ0I6hKLFPFT53JtGN3lkdYgEDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"32229783c4c8dcdb815d2df629774305ebbbc4a9aa2456283a01c4abb47e9f2f","last_reissued_at":"2026-07-05T04:06:45.172862Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:06:45.172862Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.09416","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:06:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oTA+tCl1UU+HgjoOvPerYYIum1xq5dM1gsOt9SVAKcu/f6Vg4TGbiD5f2MMI+08JnDP5go0MKcdfO9smruwuAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T10:07:37.164029Z"},"content_sha256":"b343323ef691a8cc3db1085d035355a4ecec8fbd4d44fd1ca033d827ba448d30","schema_version":"1.0","event_id":"sha256:b343323ef691a8cc3db1085d035355a4ecec8fbd4d44fd1ca033d827ba448d30"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:GIRJPA6EZDONXAK5FX3CS52DAX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ElasticFace: Elastic Margin Loss for Deep Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arjan Kuijper, Fadi Boutros, Florian Kirchbuchner, Naser Damer","submitted_at":"2021-09-20T10:31:50Z","abstract_excerpt":"Learning discriminative face features plays a major role in building high-performing face recognition models. The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation. Marginal penalty softmax losses, such as ArcFace and CosFace, assume that the geodesic distance between and within the different identities can be equ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.09416","kind":"arxiv","version":4},"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/2109.09416/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":"2026-07-05T04:06:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rPziDdsxRXS6JqfEFDlnJC/Z0vs6ER55SeA1U9Xb+Y7+ETfai7oMHoN3p4fJs3IpPKGu/gpbTC5NbXWwol4sDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T10:07:37.164930Z"},"content_sha256":"6d629dcd313b29ac74b71cc980da184d1e4dad9b288b0bf39b826259f89e9daf","schema_version":"1.0","event_id":"sha256:6d629dcd313b29ac74b71cc980da184d1e4dad9b288b0bf39b826259f89e9daf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GIRJPA6EZDONXAK5FX3CS52DAX/bundle.json","state_url":"https://pith.science/pith/GIRJPA6EZDONXAK5FX3CS52DAX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GIRJPA6EZDONXAK5FX3CS52DAX/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":"2026-08-19T10:07:37Z","links":{"resolver":"https://pith.science/pith/GIRJPA6EZDONXAK5FX3CS52DAX","bundle":"https://pith.science/pith/GIRJPA6EZDONXAK5FX3CS52DAX/bundle.json","state":"https://pith.science/pith/GIRJPA6EZDONXAK5FX3CS52DAX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GIRJPA6EZDONXAK5FX3CS52DAX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:GIRJPA6EZDONXAK5FX3CS52DAX","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":"aa1f29a17fccd7223b4e414615b780f67cb0901bfcacdd8aa389bbcdc899ca62","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-20T10:31:50Z","title_canon_sha256":"6cd5ad1c93da74e25bcd93fc238b739f948db9c117468c4a56300221c53a0668"},"schema_version":"1.0","source":{"id":"2109.09416","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.09416","created_at":"2026-07-05T04:06:45Z"},{"alias_kind":"arxiv_version","alias_value":"2109.09416v4","created_at":"2026-07-05T04:06:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.09416","created_at":"2026-07-05T04:06:45Z"},{"alias_kind":"pith_short_12","alias_value":"GIRJPA6EZDON","created_at":"2026-07-05T04:06:45Z"},{"alias_kind":"pith_short_16","alias_value":"GIRJPA6EZDONXAK5","created_at":"2026-07-05T04:06:45Z"},{"alias_kind":"pith_short_8","alias_value":"GIRJPA6E","created_at":"2026-07-05T04:06:45Z"}],"graph_snapshots":[{"event_id":"sha256:6d629dcd313b29ac74b71cc980da184d1e4dad9b288b0bf39b826259f89e9daf","target":"graph","created_at":"2026-07-05T04:06:45Z","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/2109.09416/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning discriminative face features plays a major role in building high-performing face recognition models. The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation. Marginal penalty softmax losses, such as ArcFace and CosFace, assume that the geodesic distance between and within the different identities can be equ","authors_text":"Arjan Kuijper, Fadi Boutros, Florian Kirchbuchner, Naser Damer","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-20T10:31:50Z","title":"ElasticFace: Elastic Margin Loss for Deep Face Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.09416","kind":"arxiv","version":4},"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:b343323ef691a8cc3db1085d035355a4ecec8fbd4d44fd1ca033d827ba448d30","target":"record","created_at":"2026-07-05T04:06:45Z","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":"aa1f29a17fccd7223b4e414615b780f67cb0901bfcacdd8aa389bbcdc899ca62","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-20T10:31:50Z","title_canon_sha256":"6cd5ad1c93da74e25bcd93fc238b739f948db9c117468c4a56300221c53a0668"},"schema_version":"1.0","source":{"id":"2109.09416","kind":"arxiv","version":4}},"canonical_sha256":"32229783c4c8dcdb815d2df629774305ebbbc4a9aa2456283a01c4abb47e9f2f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"32229783c4c8dcdb815d2df629774305ebbbc4a9aa2456283a01c4abb47e9f2f","first_computed_at":"2026-07-05T04:06:45.172862Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:06:45.172862Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NjMX420wZVvctd5Pl0ek1GrGYKO8V4cBFGqo7iI/hsJKnpb6qd16OBUcQlhAQ0I6hKLFPFT53JtGN3lkdYgEDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:06:45.173251Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.09416","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b343323ef691a8cc3db1085d035355a4ecec8fbd4d44fd1ca033d827ba448d30","sha256:6d629dcd313b29ac74b71cc980da184d1e4dad9b288b0bf39b826259f89e9daf"],"state_sha256":"3a4762fd87c6013cd11d4fe90eea85a2f0b839ff82b27ee674254b63822ca459"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qZo0vVQ2ou1M/L40S+aBwZDFSZyV8o8d3ZJzakUdvNXygikTUaoTb9CRG2LGoIix3Z9eyvIhqGwjxEIxPKSKDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T10:07:37.170704Z","bundle_sha256":"b5d2c82434fb7474d0676bb7aea620500858a8809a10c8a686fd1941fc941890"}}