{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:U7347YUOKY6526KJ4NXJXUH4QW","short_pith_number":"pith:U7347YUO","canonical_record":{"source":{"id":"1803.11095","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-29T14:29:46Z","cross_cats_sorted":[],"title_canon_sha256":"d4086e588fcb94cee8464886645ae06cec9ed6e48493952609a0fe72323867f7","abstract_canon_sha256":"e46b6e35b335d0381698e2b60ececd6e1a6b674da0ce08f1defd04acdafef19b"},"schema_version":"1.0"},"canonical_sha256":"a7f7cfe28e563ddd7949e36e9bd0fc85a3f595228a221df2d2b16e0577ba7315","source":{"kind":"arxiv","id":"1803.11095","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.11095","created_at":"2026-05-18T00:19:48Z"},{"alias_kind":"arxiv_version","alias_value":"1803.11095v1","created_at":"2026-05-18T00:19:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.11095","created_at":"2026-05-18T00:19:48Z"},{"alias_kind":"pith_short_12","alias_value":"U7347YUOKY65","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"U7347YUOKY6526KJ","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"U7347YUO","created_at":"2026-05-18T12:32:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:U7347YUOKY6526KJ4NXJXUH4QW","target":"record","payload":{"canonical_record":{"source":{"id":"1803.11095","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-29T14:29:46Z","cross_cats_sorted":[],"title_canon_sha256":"d4086e588fcb94cee8464886645ae06cec9ed6e48493952609a0fe72323867f7","abstract_canon_sha256":"e46b6e35b335d0381698e2b60ececd6e1a6b674da0ce08f1defd04acdafef19b"},"schema_version":"1.0"},"canonical_sha256":"a7f7cfe28e563ddd7949e36e9bd0fc85a3f595228a221df2d2b16e0577ba7315","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:48.583764Z","signature_b64":"a+SRoO244+s01RrnXn0slc8p5SlGtWJUNVxbfeCEYwnzj2XRf3YwTv+K9vjkNKtifbo+paViV2Fwlietj8hHDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7f7cfe28e563ddd7949e36e9bd0fc85a3f595228a221df2d2b16e0577ba7315","last_reissued_at":"2026-05-18T00:19:48.583012Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:48.583012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.11095","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":"2026-05-18T00:19:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uFqWM3cVDxQvYyIiyEUvlFx9whefpJZ5Eo1bSAsoirTyWp1NbgNZcAdJcVXvvcWnjlTE4kGqkJeOvmtKptkxBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T04:05:52.843568Z"},"content_sha256":"f4a327ec5d4f0bb342c3829d0f3b97c5c595c3216f322431392a979b01e6befd","schema_version":"1.0","event_id":"sha256:f4a327ec5d4f0bb342c3829d0f3b97c5c595c3216f322431392a979b01e6befd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:U7347YUOKY6526KJ4NXJXUH4QW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mining on Manifolds: Metric Learning without Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmet Iscen, Giorgos Tolias, Ondrej Chum, Yannis Avrithis","submitted_at":"2018-03-29T14:29:46Z","abstract_excerpt":"In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g. by pre-trained CNN. Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds. Both types of examples are revealed by disagreements between Euclidean and manifold similarities. The discovered examples can be used in training with any discriminative loss. The method is applied to unsupervised fine-tu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.11095","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":""},"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":"2026-05-18T00:19:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e1EJqBtxL25froqD9WMypbM96JEsUhyqv/bZERbLvRYBW5krxkyjzHxvoXkpm84AhObYJL4c50s1vJRC007GDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T04:05:52.843924Z"},"content_sha256":"c930ca676c5232829aec768d6e249208442739e843b1e76656528433d668572c","schema_version":"1.0","event_id":"sha256:c930ca676c5232829aec768d6e249208442739e843b1e76656528433d668572c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U7347YUOKY6526KJ4NXJXUH4QW/bundle.json","state_url":"https://pith.science/pith/U7347YUOKY6526KJ4NXJXUH4QW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U7347YUOKY6526KJ4NXJXUH4QW/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":"2026-06-12T04:05:52Z","links":{"resolver":"https://pith.science/pith/U7347YUOKY6526KJ4NXJXUH4QW","bundle":"https://pith.science/pith/U7347YUOKY6526KJ4NXJXUH4QW/bundle.json","state":"https://pith.science/pith/U7347YUOKY6526KJ4NXJXUH4QW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U7347YUOKY6526KJ4NXJXUH4QW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:U7347YUOKY6526KJ4NXJXUH4QW","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":"e46b6e35b335d0381698e2b60ececd6e1a6b674da0ce08f1defd04acdafef19b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-29T14:29:46Z","title_canon_sha256":"d4086e588fcb94cee8464886645ae06cec9ed6e48493952609a0fe72323867f7"},"schema_version":"1.0","source":{"id":"1803.11095","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.11095","created_at":"2026-05-18T00:19:48Z"},{"alias_kind":"arxiv_version","alias_value":"1803.11095v1","created_at":"2026-05-18T00:19:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.11095","created_at":"2026-05-18T00:19:48Z"},{"alias_kind":"pith_short_12","alias_value":"U7347YUOKY65","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"U7347YUOKY6526KJ","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"U7347YUO","created_at":"2026-05-18T12:32:56Z"}],"graph_snapshots":[{"event_id":"sha256:c930ca676c5232829aec768d6e249208442739e843b1e76656528433d668572c","target":"graph","created_at":"2026-05-18T00:19:48Z","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"},"paper":{"abstract_excerpt":"In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g. by pre-trained CNN. Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds. Both types of examples are revealed by disagreements between Euclidean and manifold similarities. The discovered examples can be used in training with any discriminative loss. The method is applied to unsupervised fine-tu","authors_text":"Ahmet Iscen, Giorgos Tolias, Ondrej Chum, Yannis Avrithis","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-29T14:29:46Z","title":"Mining on Manifolds: Metric Learning without Labels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.11095","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:f4a327ec5d4f0bb342c3829d0f3b97c5c595c3216f322431392a979b01e6befd","target":"record","created_at":"2026-05-18T00:19:48Z","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":"e46b6e35b335d0381698e2b60ececd6e1a6b674da0ce08f1defd04acdafef19b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-29T14:29:46Z","title_canon_sha256":"d4086e588fcb94cee8464886645ae06cec9ed6e48493952609a0fe72323867f7"},"schema_version":"1.0","source":{"id":"1803.11095","kind":"arxiv","version":1}},"canonical_sha256":"a7f7cfe28e563ddd7949e36e9bd0fc85a3f595228a221df2d2b16e0577ba7315","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7f7cfe28e563ddd7949e36e9bd0fc85a3f595228a221df2d2b16e0577ba7315","first_computed_at":"2026-05-18T00:19:48.583012Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:48.583012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"a+SRoO244+s01RrnXn0slc8p5SlGtWJUNVxbfeCEYwnzj2XRf3YwTv+K9vjkNKtifbo+paViV2Fwlietj8hHDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:48.583764Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.11095","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f4a327ec5d4f0bb342c3829d0f3b97c5c595c3216f322431392a979b01e6befd","sha256:c930ca676c5232829aec768d6e249208442739e843b1e76656528433d668572c"],"state_sha256":"a7a0e8f04f1124df42eef22d217715bc9973eebb50ca52d41801ec45e31a4a3d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WjapBSnBSy4W5Gc4Bpv55javHBvvtR6n0dfkQOyS0yMP7zpByHeYhP/54vJrhjk+S9uq8q5+emzLoT5pWMt+Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T04:05:52.845961Z","bundle_sha256":"09ce6e33f6bf86b799a06745b373d0c8a38715041a435cd491b09aa1140d3cb9"}}