{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:VQHM232WGPLVEMFIBGZMKK63ZY","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":"748ecc80d8fca27b2e9d3bfcbabb2b352113ab532afe32ac96966ad45f48093e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-02-14T23:18:12Z","title_canon_sha256":"aea50ceaed0766275caded2c458c8f86771c5acbd5c71d2abfa864a5da8ef3cc"},"schema_version":"1.0","source":{"id":"2102.07265","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.07265","created_at":"2026-07-05T02:15:14Z"},{"alias_kind":"arxiv_version","alias_value":"2102.07265v1","created_at":"2026-07-05T02:15:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.07265","created_at":"2026-07-05T02:15:14Z"},{"alias_kind":"pith_short_12","alias_value":"VQHM232WGPLV","created_at":"2026-07-05T02:15:14Z"},{"alias_kind":"pith_short_16","alias_value":"VQHM232WGPLVEMFI","created_at":"2026-07-05T02:15:14Z"},{"alias_kind":"pith_short_8","alias_value":"VQHM232W","created_at":"2026-07-05T02:15:14Z"}],"graph_snapshots":[{"event_id":"sha256:e326f721ce514246e4810c502c1e4132d5f72455f4496084bee89c934136759e","target":"graph","created_at":"2026-07-05T02:15:14Z","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/2102.07265/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep Metric Learning (DML), a widely-used technique, involves learning a distance metric between pairs of samples. DML uses deep neural architectures to learn semantic embeddings of the input, where the distance between similar examples is small while dissimilar ones are far apart. Although the underlying neural networks produce good accuracy on naturally occurring samples, they are vulnerable to adversarially-perturbed samples that reduce performance. We take a first step towards training robust DML models and tackle the primary challenge of the metric losses being dependent on the samples in","authors_text":"Earlence Fernandes, Pengyu Kan, Somesh Jha, Thomas Kobber Panum, Zi Wang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-02-14T23:18:12Z","title":"Exploring Adversarial Robustness of Deep Metric Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.07265","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":"sha256:838deceb6c995239157b1c687b7e0314877700c191985c5710f7a34bfb19ab54","target":"record","created_at":"2026-07-05T02:15:14Z","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":"748ecc80d8fca27b2e9d3bfcbabb2b352113ab532afe32ac96966ad45f48093e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-02-14T23:18:12Z","title_canon_sha256":"aea50ceaed0766275caded2c458c8f86771c5acbd5c71d2abfa864a5da8ef3cc"},"schema_version":"1.0","source":{"id":"2102.07265","kind":"arxiv","version":1}},"canonical_sha256":"ac0ecd6f5633d75230a809b2c52bdbce09d6034a6a7506d70270ac3e08246791","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ac0ecd6f5633d75230a809b2c52bdbce09d6034a6a7506d70270ac3e08246791","first_computed_at":"2026-07-05T02:15:14.263835Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:15:14.263835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gy+0xZeMScNTvuLTlcwhXdlYcYIuF45oQwI4Vn5abJ7HC3/PhZXS+9KNvDZMPKt/3pVNNPww5iimSHjNFMG0Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T02:15:14.264187Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.07265","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:838deceb6c995239157b1c687b7e0314877700c191985c5710f7a34bfb19ab54","sha256:e326f721ce514246e4810c502c1e4132d5f72455f4496084bee89c934136759e"],"state_sha256":"db7363e53bf9db320cdfdd4228499ef8f4338cf7ecc02156a3e8bf512e47ac3b"}