{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:OFXOBJ6BDSDRPN4K2G6ASDDURE","short_pith_number":"pith:OFXOBJ6B","canonical_record":{"source":{"id":"1909.03909","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-09T15:04:26Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"670de7faa1b1510ac12937a8948e73582323eaefa88840691519ef01687725b8","abstract_canon_sha256":"1722ba5b7472c312742d4113d2c1f146b36ce68293755220d3280d6b16ffb171"},"schema_version":"1.0"},"canonical_sha256":"716ee0a7c11c8717b78ad1bc090c748915b501a6b501ee64134af077d54f6314","source":{"kind":"arxiv","id":"1909.03909","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.03909","created_at":"2026-07-05T00:03:09Z"},{"alias_kind":"arxiv_version","alias_value":"1909.03909v1","created_at":"2026-07-05T00:03:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.03909","created_at":"2026-07-05T00:03:09Z"},{"alias_kind":"pith_short_12","alias_value":"OFXOBJ6BDSDR","created_at":"2026-07-05T00:03:09Z"},{"alias_kind":"pith_short_16","alias_value":"OFXOBJ6BDSDRPN4K","created_at":"2026-07-05T00:03:09Z"},{"alias_kind":"pith_short_8","alias_value":"OFXOBJ6B","created_at":"2026-07-05T00:03:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:OFXOBJ6BDSDRPN4K2G6ASDDURE","target":"record","payload":{"canonical_record":{"source":{"id":"1909.03909","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-09T15:04:26Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"670de7faa1b1510ac12937a8948e73582323eaefa88840691519ef01687725b8","abstract_canon_sha256":"1722ba5b7472c312742d4113d2c1f146b36ce68293755220d3280d6b16ffb171"},"schema_version":"1.0"},"canonical_sha256":"716ee0a7c11c8717b78ad1bc090c748915b501a6b501ee64134af077d54f6314","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:03:09.717089Z","signature_b64":"ZwTzObD+1ifhhZSkOWYdMBSp27YjG18R7O0c0QbFlIpiMIh2Gb6EKxfjGQhrCFgMkjDcPlSEVm3p1J2M2/HtBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"716ee0a7c11c8717b78ad1bc090c748915b501a6b501ee64134af077d54f6314","last_reissued_at":"2026-07-05T00:03:09.716748Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:03:09.716748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.03909","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":"2026-07-05T00:03:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ElQjNu0WqK8q5okVZiIpJptwj0AKS5XXS4cErzs64OVUh4eTd70IP95CYZ0VRglf/JxCI/DSO7BiSfL/iR4cAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T07:16:30.840642Z"},"content_sha256":"bcc7615ac24a30e8a71c2a3529e19dc495bbdfb8cc3470de9d192e64061d6041","schema_version":"1.0","event_id":"sha256:bcc7615ac24a30e8a71c2a3529e19dc495bbdfb8cc3470de9d192e64061d6041"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:OFXOBJ6BDSDRPN4K2G6ASDDURE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Metric Learning with Density Adaptivity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Hongyang Chao, Tao Mei, Ting Yao, Yehao Li, Yingwei Pan","submitted_at":"2019-09-09T15:04:26Z","abstract_excerpt":"The problem of distance metric learning is mostly considered from the perspective of learning an embedding space, where the distances between pairs of examples are in correspondence with a similarity metric. With the rise and success of Convolutional Neural Networks (CNN), deep metric learning (DML) involves training a network to learn a nonlinear transformation to the embedding space. Existing DML approaches often express the supervision through maximizing inter-class distance and minimizing intra-class variation. However, the results can suffer from overfitting problem, especially when the t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.03909","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/1909.03909/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":"2026-07-05T00:03:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hc6btgJLWzKFT1aJ5OvLTgFPMCrxC78zA14VQbY0Ly03z1ISqITLA4JSrTQdcc2WyLwIj+3eLe4JZk/dTthBCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T07:16:30.841190Z"},"content_sha256":"6a84e689608c7f652e25bb7efcbb85dd60c04772aabb94e2011dd7c6d0378717","schema_version":"1.0","event_id":"sha256:6a84e689608c7f652e25bb7efcbb85dd60c04772aabb94e2011dd7c6d0378717"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OFXOBJ6BDSDRPN4K2G6ASDDURE/bundle.json","state_url":"https://pith.science/pith/OFXOBJ6BDSDRPN4K2G6ASDDURE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OFXOBJ6BDSDRPN4K2G6ASDDURE/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":"2026-08-07T07:16:30Z","links":{"resolver":"https://pith.science/pith/OFXOBJ6BDSDRPN4K2G6ASDDURE","bundle":"https://pith.science/pith/OFXOBJ6BDSDRPN4K2G6ASDDURE/bundle.json","state":"https://pith.science/pith/OFXOBJ6BDSDRPN4K2G6ASDDURE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OFXOBJ6BDSDRPN4K2G6ASDDURE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:OFXOBJ6BDSDRPN4K2G6ASDDURE","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":"1722ba5b7472c312742d4113d2c1f146b36ce68293755220d3280d6b16ffb171","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-09T15:04:26Z","title_canon_sha256":"670de7faa1b1510ac12937a8948e73582323eaefa88840691519ef01687725b8"},"schema_version":"1.0","source":{"id":"1909.03909","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.03909","created_at":"2026-07-05T00:03:09Z"},{"alias_kind":"arxiv_version","alias_value":"1909.03909v1","created_at":"2026-07-05T00:03:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.03909","created_at":"2026-07-05T00:03:09Z"},{"alias_kind":"pith_short_12","alias_value":"OFXOBJ6BDSDR","created_at":"2026-07-05T00:03:09Z"},{"alias_kind":"pith_short_16","alias_value":"OFXOBJ6BDSDRPN4K","created_at":"2026-07-05T00:03:09Z"},{"alias_kind":"pith_short_8","alias_value":"OFXOBJ6B","created_at":"2026-07-05T00:03:09Z"}],"graph_snapshots":[{"event_id":"sha256:6a84e689608c7f652e25bb7efcbb85dd60c04772aabb94e2011dd7c6d0378717","target":"graph","created_at":"2026-07-05T00:03:09Z","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/1909.03909/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The problem of distance metric learning is mostly considered from the perspective of learning an embedding space, where the distances between pairs of examples are in correspondence with a similarity metric. With the rise and success of Convolutional Neural Networks (CNN), deep metric learning (DML) involves training a network to learn a nonlinear transformation to the embedding space. Existing DML approaches often express the supervision through maximizing inter-class distance and minimizing intra-class variation. However, the results can suffer from overfitting problem, especially when the t","authors_text":"Hongyang Chao, Tao Mei, Ting Yao, Yehao Li, Yingwei Pan","cross_cats":["cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-09T15:04:26Z","title":"Deep Metric Learning with Density Adaptivity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.03909","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:bcc7615ac24a30e8a71c2a3529e19dc495bbdfb8cc3470de9d192e64061d6041","target":"record","created_at":"2026-07-05T00:03:09Z","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":"1722ba5b7472c312742d4113d2c1f146b36ce68293755220d3280d6b16ffb171","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-09T15:04:26Z","title_canon_sha256":"670de7faa1b1510ac12937a8948e73582323eaefa88840691519ef01687725b8"},"schema_version":"1.0","source":{"id":"1909.03909","kind":"arxiv","version":1}},"canonical_sha256":"716ee0a7c11c8717b78ad1bc090c748915b501a6b501ee64134af077d54f6314","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"716ee0a7c11c8717b78ad1bc090c748915b501a6b501ee64134af077d54f6314","first_computed_at":"2026-07-05T00:03:09.716748Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:03:09.716748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZwTzObD+1ifhhZSkOWYdMBSp27YjG18R7O0c0QbFlIpiMIh2Gb6EKxfjGQhrCFgMkjDcPlSEVm3p1J2M2/HtBw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:03:09.717089Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.03909","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bcc7615ac24a30e8a71c2a3529e19dc495bbdfb8cc3470de9d192e64061d6041","sha256:6a84e689608c7f652e25bb7efcbb85dd60c04772aabb94e2011dd7c6d0378717"],"state_sha256":"e5d8398b0c1819a59bf33da4b079906da5613c663bd0d86a74429ffaa40a357e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IWrUkVU+a0QJWnTTjCnN4JRMg9wzgBTeE9y7PwMV7sQ+DaZfBy3HbxcLJkwoMun0RNnWZrU+G7e4GthvrpMdCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T07:16:30.845100Z","bundle_sha256":"5616a41f318b8cda930c45e6f12dd6c5e84d1392f836d98642baa4f83dbb385f"}}