{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:476XQ7SL3ZCQPWV2KMR6EXH4GW","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":"1ab8f584c25a701dbd8c29cb347a52f5e752a7c17acb22fbd0da4f3a1acf953c","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-17T00:49:12Z","title_canon_sha256":"8f04a76475324a0b21440d35a82563c9917cdf368a1885689df31bf355423d82"},"schema_version":"1.0","source":{"id":"2412.12432","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.12432","created_at":"2026-07-05T09:50:14Z"},{"alias_kind":"arxiv_version","alias_value":"2412.12432v1","created_at":"2026-07-05T09:50:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12432","created_at":"2026-07-05T09:50:14Z"},{"alias_kind":"pith_short_12","alias_value":"476XQ7SL3ZCQ","created_at":"2026-07-05T09:50:14Z"},{"alias_kind":"pith_short_16","alias_value":"476XQ7SL3ZCQPWV2","created_at":"2026-07-05T09:50:14Z"},{"alias_kind":"pith_short_8","alias_value":"476XQ7SL","created_at":"2026-07-05T09:50:14Z"}],"graph_snapshots":[{"event_id":"sha256:a9770f2563ccd5491a86657df1d23634e87c84e3fd8e18b0c624ed48b07b0f17","target":"graph","created_at":"2026-07-05T09:50:14Z","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/2412.12432/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper addresses supervised deep metric learning for open-set image retrieval, focusing on three key aspects: the loss function, mixup regularization, and model initialization. In deep metric learning, optimizing the retrieval evaluation metric, recall@k, via gradient descent is desirable but challenging due to its non-differentiable nature. To overcome this, we propose a differentiable surrogate loss that is computed on large batches, nearly equivalent to the entire training set. This computationally intensive process is made feasible through an implementation that bypasses the GPU memory","authors_text":"Giorgos Tolias, Jiri Matas, Yash Patel","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-17T00:49:12Z","title":"Three Things to Know about Deep Metric Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12432","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:f58678f4ec056027702c23ae9740264ab77a599db857d7f0103716df89b91be2","target":"record","created_at":"2026-07-05T09:50:14Z","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":"1ab8f584c25a701dbd8c29cb347a52f5e752a7c17acb22fbd0da4f3a1acf953c","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-17T00:49:12Z","title_canon_sha256":"8f04a76475324a0b21440d35a82563c9917cdf368a1885689df31bf355423d82"},"schema_version":"1.0","source":{"id":"2412.12432","kind":"arxiv","version":1}},"canonical_sha256":"e7fd787e4bde4507daba5323e25cfc35a5a324d94f21742f45fd35c628605099","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e7fd787e4bde4507daba5323e25cfc35a5a324d94f21742f45fd35c628605099","first_computed_at":"2026-07-05T09:50:14.742192Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:50:14.742192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z4rEcgHi7hEmuHDsWPmztalcvAg4xcUTaCS9PTXlW+tQcFWb8axlI0CMDlxBhJ7o7vxRLXDzEdel//E+373RDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:50:14.742607Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.12432","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f58678f4ec056027702c23ae9740264ab77a599db857d7f0103716df89b91be2","sha256:a9770f2563ccd5491a86657df1d23634e87c84e3fd8e18b0c624ed48b07b0f17"],"state_sha256":"7b932223029c3acc8198aeddc1293b0bc3d7db6a1a269a23012360ad135ba218"}