{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ZY4VPTKWH3HFMMBOUDTQ2FHYCL","short_pith_number":"pith:ZY4VPTKW","canonical_record":{"source":{"id":"2210.04567","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-10T11:12:24Z","cross_cats_sorted":[],"title_canon_sha256":"da29b3b864cfafe01761d0e12867fa58b17247a5ccf830e5dc337318ebf5837c","abstract_canon_sha256":"875532c1d0eda7104ad47c3f662367c66cfdce51104fa750224282ce4e545377"},"schema_version":"1.0"},"canonical_sha256":"ce3957cd563ece56302ea0e70d14f812caf666ef56fd53712a2be803e6460b9b","source":{"kind":"arxiv","id":"2210.04567","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.04567","created_at":"2026-07-05T05:04:54Z"},{"alias_kind":"arxiv_version","alias_value":"2210.04567v1","created_at":"2026-07-05T05:04:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.04567","created_at":"2026-07-05T05:04:54Z"},{"alias_kind":"pith_short_12","alias_value":"ZY4VPTKWH3HF","created_at":"2026-07-05T05:04:54Z"},{"alias_kind":"pith_short_16","alias_value":"ZY4VPTKWH3HFMMBO","created_at":"2026-07-05T05:04:54Z"},{"alias_kind":"pith_short_8","alias_value":"ZY4VPTKW","created_at":"2026-07-05T05:04:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ZY4VPTKWH3HFMMBOUDTQ2FHYCL","target":"record","payload":{"canonical_record":{"source":{"id":"2210.04567","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-10T11:12:24Z","cross_cats_sorted":[],"title_canon_sha256":"da29b3b864cfafe01761d0e12867fa58b17247a5ccf830e5dc337318ebf5837c","abstract_canon_sha256":"875532c1d0eda7104ad47c3f662367c66cfdce51104fa750224282ce4e545377"},"schema_version":"1.0"},"canonical_sha256":"ce3957cd563ece56302ea0e70d14f812caf666ef56fd53712a2be803e6460b9b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:04:54.816480Z","signature_b64":"CzHfYRt707OGkzlAzGy7cDQsF7ZKL5bUQLXGymfuJ8UgOFvCHmhGFsnZ4cAzXCm3Fv1IlMlG0+4EKGft67K3Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce3957cd563ece56302ea0e70d14f812caf666ef56fd53712a2be803e6460b9b","last_reissued_at":"2026-07-05T05:04:54.816142Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:04:54.816142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.04567","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:04:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dR5YPNnqWg8Kk3Axl1PeMpEE2YYgVYBdF4JIZ9TYO9AWFwUv4JxCZfqN719utMH2oiXDPpGwkNX9k3Z1xEmWBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T00:02:11.415864Z"},"content_sha256":"8483e9c8378559769d1cad0b6a516613812872910e99c3e84704cff65db139fb","schema_version":"1.0","event_id":"sha256:8483e9c8378559769d1cad0b6a516613812872910e99c3e84704cff65db139fb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ZY4VPTKWH3HFMMBOUDTQ2FHYCL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"BoundaryFace: A mining framework with noise label self-correction for Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Shijie Wu, Xun Gong","submitted_at":"2022-10-10T11:12:24Z","abstract_excerpt":"Face recognition has made tremendous progress in recent years due to the advances in loss functions and the explosive growth in training sets size. A properly designed loss is seen as key to extract discriminative features for classification. Several margin-based losses have been proposed as alternatives of softmax loss in face recognition. However, two issues remain to consider: 1) They overlook the importance of hard sample mining for discriminative learning. 2) Label noise ubiquitously exists in large-scale datasets, which can seriously damage the model's performance. In this paper, startin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.04567","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/2210.04567/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":"2026-07-05T05:04:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SWEy6+mYn3yLbkAJJgmv+hfYGCy6sOSSP/RG49MFDLaJkA9FJc3mTBHN7P1Bb+Ith977m1+iw6b7pZePgtL2BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T00:02:11.416804Z"},"content_sha256":"1e620d84c170eab9620ca0927c7716e254fb49d98d1de92ffba85070284fb30b","schema_version":"1.0","event_id":"sha256:1e620d84c170eab9620ca0927c7716e254fb49d98d1de92ffba85070284fb30b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZY4VPTKWH3HFMMBOUDTQ2FHYCL/bundle.json","state_url":"https://pith.science/pith/ZY4VPTKWH3HFMMBOUDTQ2FHYCL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZY4VPTKWH3HFMMBOUDTQ2FHYCL/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":"2026-08-07T00:02:11Z","links":{"resolver":"https://pith.science/pith/ZY4VPTKWH3HFMMBOUDTQ2FHYCL","bundle":"https://pith.science/pith/ZY4VPTKWH3HFMMBOUDTQ2FHYCL/bundle.json","state":"https://pith.science/pith/ZY4VPTKWH3HFMMBOUDTQ2FHYCL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZY4VPTKWH3HFMMBOUDTQ2FHYCL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZY4VPTKWH3HFMMBOUDTQ2FHYCL","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":"875532c1d0eda7104ad47c3f662367c66cfdce51104fa750224282ce4e545377","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-10T11:12:24Z","title_canon_sha256":"da29b3b864cfafe01761d0e12867fa58b17247a5ccf830e5dc337318ebf5837c"},"schema_version":"1.0","source":{"id":"2210.04567","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.04567","created_at":"2026-07-05T05:04:54Z"},{"alias_kind":"arxiv_version","alias_value":"2210.04567v1","created_at":"2026-07-05T05:04:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.04567","created_at":"2026-07-05T05:04:54Z"},{"alias_kind":"pith_short_12","alias_value":"ZY4VPTKWH3HF","created_at":"2026-07-05T05:04:54Z"},{"alias_kind":"pith_short_16","alias_value":"ZY4VPTKWH3HFMMBO","created_at":"2026-07-05T05:04:54Z"},{"alias_kind":"pith_short_8","alias_value":"ZY4VPTKW","created_at":"2026-07-05T05:04:54Z"}],"graph_snapshots":[{"event_id":"sha256:1e620d84c170eab9620ca0927c7716e254fb49d98d1de92ffba85070284fb30b","target":"graph","created_at":"2026-07-05T05:04:54Z","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/2210.04567/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Face recognition has made tremendous progress in recent years due to the advances in loss functions and the explosive growth in training sets size. A properly designed loss is seen as key to extract discriminative features for classification. Several margin-based losses have been proposed as alternatives of softmax loss in face recognition. However, two issues remain to consider: 1) They overlook the importance of hard sample mining for discriminative learning. 2) Label noise ubiquitously exists in large-scale datasets, which can seriously damage the model's performance. In this paper, startin","authors_text":"Shijie Wu, Xun Gong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-10T11:12:24Z","title":"BoundaryFace: A mining framework with noise label self-correction for Face Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.04567","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":"sha256:8483e9c8378559769d1cad0b6a516613812872910e99c3e84704cff65db139fb","target":"record","created_at":"2026-07-05T05:04:54Z","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":"875532c1d0eda7104ad47c3f662367c66cfdce51104fa750224282ce4e545377","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-10T11:12:24Z","title_canon_sha256":"da29b3b864cfafe01761d0e12867fa58b17247a5ccf830e5dc337318ebf5837c"},"schema_version":"1.0","source":{"id":"2210.04567","kind":"arxiv","version":1}},"canonical_sha256":"ce3957cd563ece56302ea0e70d14f812caf666ef56fd53712a2be803e6460b9b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ce3957cd563ece56302ea0e70d14f812caf666ef56fd53712a2be803e6460b9b","first_computed_at":"2026-07-05T05:04:54.816142Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:04:54.816142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CzHfYRt707OGkzlAzGy7cDQsF7ZKL5bUQLXGymfuJ8UgOFvCHmhGFsnZ4cAzXCm3Fv1IlMlG0+4EKGft67K3Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:04:54.816480Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.04567","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8483e9c8378559769d1cad0b6a516613812872910e99c3e84704cff65db139fb","sha256:1e620d84c170eab9620ca0927c7716e254fb49d98d1de92ffba85070284fb30b"],"state_sha256":"cb5f65cf8d3d8dcbe0f06894e8f8db801158cc34e4b921a73cbc059b557bb43c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s9BGN55adWwJ12EK+8wXrhgS1VwEdwI0juEXApjFfCDz0KIu0sSQ5qyVqPlgDKEFzFXyUKTxw5b2sO4uRxcLAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T00:02:11.505833Z","bundle_sha256":"0fecd9067832a51bd5b49f00921a04e41d6c9b0b318a03207a8fa1529dc97062"}}