{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:TKLMJT64Y5XURFT65JVJVQYZCW","short_pith_number":"pith:TKLMJT64","canonical_record":{"source":{"id":"1901.08339","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-24T10:46:41Z","cross_cats_sorted":[],"title_canon_sha256":"b3866d99e68948b9b3b157eb92b9f77ab0ca9021498ad057cf70aeb032ecf78c","abstract_canon_sha256":"6d88bb847a0c2b5d19bc8d62d3f8219adaa8266ab7fa350cabacd0f2d8698c56"},"schema_version":"1.0"},"canonical_sha256":"9a96c4cfdcc76f48967eea6a9ac31915822506e1f0fd5c19b62d7621b189bb20","source":{"kind":"arxiv","id":"1901.08339","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.08339","created_at":"2026-05-17T23:55:37Z"},{"alias_kind":"arxiv_version","alias_value":"1901.08339v1","created_at":"2026-05-17T23:55:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.08339","created_at":"2026-05-17T23:55:37Z"},{"alias_kind":"pith_short_12","alias_value":"TKLMJT64Y5XU","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"TKLMJT64Y5XURFT6","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"TKLMJT64","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:TKLMJT64Y5XURFT65JVJVQYZCW","target":"record","payload":{"canonical_record":{"source":{"id":"1901.08339","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-24T10:46:41Z","cross_cats_sorted":[],"title_canon_sha256":"b3866d99e68948b9b3b157eb92b9f77ab0ca9021498ad057cf70aeb032ecf78c","abstract_canon_sha256":"6d88bb847a0c2b5d19bc8d62d3f8219adaa8266ab7fa350cabacd0f2d8698c56"},"schema_version":"1.0"},"canonical_sha256":"9a96c4cfdcc76f48967eea6a9ac31915822506e1f0fd5c19b62d7621b189bb20","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:37.283362Z","signature_b64":"nmwUlL1vMhDZCI8v2h+DUKRy7dSiIXp53IygzjPMi3jBePlC+4UMV47GO4Uz6U/vTufP12KZvaLuG5i93alPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a96c4cfdcc76f48967eea6a9ac31915822506e1f0fd5c19b62d7621b189bb20","last_reissued_at":"2026-05-17T23:55:37.282830Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:37.282830Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.08339","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":"2026-05-17T23:55:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3EicqCnbg117JKybXUyKrokLWrxnarIkNzdzwZHr1/ZZvt+ofOzpI12MCu/eoAbTIFBHXFkdKi2e6K3HZ6Y2Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T09:50:39.831061Z"},"content_sha256":"3bde88cc37d1a03c0cc4fc6a2eae8ca34cfec3213b2bd3a89f7215046b4835c5","schema_version":"1.0","event_id":"sha256:3bde88cc37d1a03c0cc4fc6a2eae8ca34cfec3213b2bd3a89f7215046b4835c5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:TKLMJT64Y5XURFT65JVJVQYZCW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-Supervised Semantic Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Juho Kannala, Zakaria Laskar","submitted_at":"2019-01-24T10:46:41Z","abstract_excerpt":"Convolutional neural networks (CNNs) have been successfully applied to solve the problem of correspondence estimation between semantically related images. Due to non-availability of large training datasets, existing methods resort to self-supervised or unsupervised training paradigm. In this paper we propose a semi-supervised learning framework that imposes cyclic consistency constraint on unlabeled image pairs. Together with the supervised loss the proposed model achieves state-of-the-art on a benchmark semantic matching dataset."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.08339","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":"2026-05-17T23:55:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ahAtvrQn5TkUiwQ3XkKRLnGjlTKwkuzJWeway+qmurL8RmaHFA7vZFsjjQKkYnU5TofdMMGciU7eczTnjDx9DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T09:50:39.831414Z"},"content_sha256":"d35a55aa6dc43f63a36ec21cfb5f457b46bc19fa73245bf6d798668c0aa82b11","schema_version":"1.0","event_id":"sha256:d35a55aa6dc43f63a36ec21cfb5f457b46bc19fa73245bf6d798668c0aa82b11"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TKLMJT64Y5XURFT65JVJVQYZCW/bundle.json","state_url":"https://pith.science/pith/TKLMJT64Y5XURFT65JVJVQYZCW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TKLMJT64Y5XURFT65JVJVQYZCW/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":"2026-07-04T09:50:39Z","links":{"resolver":"https://pith.science/pith/TKLMJT64Y5XURFT65JVJVQYZCW","bundle":"https://pith.science/pith/TKLMJT64Y5XURFT65JVJVQYZCW/bundle.json","state":"https://pith.science/pith/TKLMJT64Y5XURFT65JVJVQYZCW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TKLMJT64Y5XURFT65JVJVQYZCW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:TKLMJT64Y5XURFT65JVJVQYZCW","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":"6d88bb847a0c2b5d19bc8d62d3f8219adaa8266ab7fa350cabacd0f2d8698c56","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-24T10:46:41Z","title_canon_sha256":"b3866d99e68948b9b3b157eb92b9f77ab0ca9021498ad057cf70aeb032ecf78c"},"schema_version":"1.0","source":{"id":"1901.08339","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.08339","created_at":"2026-05-17T23:55:37Z"},{"alias_kind":"arxiv_version","alias_value":"1901.08339v1","created_at":"2026-05-17T23:55:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.08339","created_at":"2026-05-17T23:55:37Z"},{"alias_kind":"pith_short_12","alias_value":"TKLMJT64Y5XU","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"TKLMJT64Y5XURFT6","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"TKLMJT64","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:d35a55aa6dc43f63a36ec21cfb5f457b46bc19fa73245bf6d798668c0aa82b11","target":"graph","created_at":"2026-05-17T23:55:37Z","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":"Convolutional neural networks (CNNs) have been successfully applied to solve the problem of correspondence estimation between semantically related images. Due to non-availability of large training datasets, existing methods resort to self-supervised or unsupervised training paradigm. In this paper we propose a semi-supervised learning framework that imposes cyclic consistency constraint on unlabeled image pairs. Together with the supervised loss the proposed model achieves state-of-the-art on a benchmark semantic matching dataset.","authors_text":"Juho Kannala, Zakaria Laskar","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-24T10:46:41Z","title":"Semi-Supervised Semantic Matching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.08339","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":"sha256:3bde88cc37d1a03c0cc4fc6a2eae8ca34cfec3213b2bd3a89f7215046b4835c5","target":"record","created_at":"2026-05-17T23:55:37Z","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":"6d88bb847a0c2b5d19bc8d62d3f8219adaa8266ab7fa350cabacd0f2d8698c56","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-24T10:46:41Z","title_canon_sha256":"b3866d99e68948b9b3b157eb92b9f77ab0ca9021498ad057cf70aeb032ecf78c"},"schema_version":"1.0","source":{"id":"1901.08339","kind":"arxiv","version":1}},"canonical_sha256":"9a96c4cfdcc76f48967eea6a9ac31915822506e1f0fd5c19b62d7621b189bb20","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9a96c4cfdcc76f48967eea6a9ac31915822506e1f0fd5c19b62d7621b189bb20","first_computed_at":"2026-05-17T23:55:37.282830Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:55:37.282830Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nmwUlL1vMhDZCI8v2h+DUKRy7dSiIXp53IygzjPMi3jBePlC+4UMV47GO4Uz6U/vTufP12KZvaLuG5i93alPCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:55:37.283362Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.08339","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3bde88cc37d1a03c0cc4fc6a2eae8ca34cfec3213b2bd3a89f7215046b4835c5","sha256:d35a55aa6dc43f63a36ec21cfb5f457b46bc19fa73245bf6d798668c0aa82b11"],"state_sha256":"02fc867c6cde4928e31b1b7b2d386e81658cf31e9d468caaf8d8b92bda269827"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7NIvtB6mzgYqGfY8TR/DliAsIzeItzjmY3SOFdknvqJ2Wc5W4717+uVQgsHqeVIBJ0xZ5/rYVrs682gcYEh4BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T09:50:39.833357Z","bundle_sha256":"04b04243d05ea81a525ee06b8616a6868fd6b60fc9b689939824e695a3801d09"}}