{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:6BXBEBGFBPHXYOOF2DDCSBEYFR","short_pith_number":"pith:6BXBEBGF","canonical_record":{"source":{"id":"2203.06915","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-14T08:08:48Z","cross_cats_sorted":[],"title_canon_sha256":"21d2566baf0f5172d7ee19a7316b8aee4142221e51b06566e388709717806642","abstract_canon_sha256":"595af3e828660cf825dc58897d0991f7301cec0bdbea2b2601e44f18ac2f0573"},"schema_version":"1.0"},"canonical_sha256":"f06e1204c50bcf7c39c5d0c62904982c7262342531a681ff63d3a55296843cf0","source":{"kind":"arxiv","id":"2203.06915","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.06915","created_at":"2026-07-05T04:06:03Z"},{"alias_kind":"arxiv_version","alias_value":"2203.06915v2","created_at":"2026-07-05T04:06:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.06915","created_at":"2026-07-05T04:06:03Z"},{"alias_kind":"pith_short_12","alias_value":"6BXBEBGFBPHX","created_at":"2026-07-05T04:06:03Z"},{"alias_kind":"pith_short_16","alias_value":"6BXBEBGFBPHXYOOF","created_at":"2026-07-05T04:06:03Z"},{"alias_kind":"pith_short_8","alias_value":"6BXBEBGF","created_at":"2026-07-05T04:06:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:6BXBEBGFBPHXYOOF2DDCSBEYFR","target":"record","payload":{"canonical_record":{"source":{"id":"2203.06915","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-14T08:08:48Z","cross_cats_sorted":[],"title_canon_sha256":"21d2566baf0f5172d7ee19a7316b8aee4142221e51b06566e388709717806642","abstract_canon_sha256":"595af3e828660cf825dc58897d0991f7301cec0bdbea2b2601e44f18ac2f0573"},"schema_version":"1.0"},"canonical_sha256":"f06e1204c50bcf7c39c5d0c62904982c7262342531a681ff63d3a55296843cf0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:06:03.003816Z","signature_b64":"cubh0T/bN164yW7E5v79VjYJIo6oqdeaevVGvWr1462k5umykUooO8G21wTZmCIBKr7WAuywl80JvGDTsBq6CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f06e1204c50bcf7c39c5d0c62904982c7262342531a681ff63d3a55296843cf0","last_reissued_at":"2026-07-05T04:06:03.003251Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:06:03.003251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.06915","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:06:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3BwiAki8N+7yNAihbHpEsqmKZBj4WR0MDDUDY+tzcCVD5Zx5BDYDLRzI3doQu3oSemOoh78TFvH1ydkTXeWDBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:30:41.166239Z"},"content_sha256":"c00f0a5e34852c141fa1c4b6174c9963add74b942196276d674dc5cc34c00902","schema_version":"1.0","event_id":"sha256:c00f0a5e34852c141fa1c4b6174c9963add74b942196276d674dc5cc34c00902"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:6BXBEBGFBPHXYOOF2DDCSBEYFR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SimMatch: Semi-supervised Learning with Similarity Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chang Xu, Chen Qian, Fei Wang, Lang Huang, Mingkai Zheng, Shan You","submitted_at":"2022-03-14T08:08:48Z","abstract_excerpt":"Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simultaneously considers semantic similarity and instance similarity. In SimMatch, the consistency regularization will be applied on both semantic-level and instance-level. The different augmented views of the same instance are encouraged to have the same class prediction and similar similarity relationship respected to other instances. Next, we instantiated a labeled memory buffer t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.06915","kind":"arxiv","version":2},"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/2203.06915/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":"2026-07-05T04:06:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I+D6/uFytW1R87RYCWp9ZPDQoIkf64SCDCDm8lCtNgZSJNffa3qq9qWIeUMyozLb6DeiLjvZB0ZiwsU5iX6YAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:30:41.166620Z"},"content_sha256":"27e1bb4f0d4dd7b129b69c9fa7b59cf4d831bad077b9a5e5cac0797ceac4f80b","schema_version":"1.0","event_id":"sha256:27e1bb4f0d4dd7b129b69c9fa7b59cf4d831bad077b9a5e5cac0797ceac4f80b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6BXBEBGFBPHXYOOF2DDCSBEYFR/bundle.json","state_url":"https://pith.science/pith/6BXBEBGFBPHXYOOF2DDCSBEYFR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6BXBEBGFBPHXYOOF2DDCSBEYFR/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":"2026-07-07T13:30:41Z","links":{"resolver":"https://pith.science/pith/6BXBEBGFBPHXYOOF2DDCSBEYFR","bundle":"https://pith.science/pith/6BXBEBGFBPHXYOOF2DDCSBEYFR/bundle.json","state":"https://pith.science/pith/6BXBEBGFBPHXYOOF2DDCSBEYFR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6BXBEBGFBPHXYOOF2DDCSBEYFR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:6BXBEBGFBPHXYOOF2DDCSBEYFR","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":"595af3e828660cf825dc58897d0991f7301cec0bdbea2b2601e44f18ac2f0573","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-14T08:08:48Z","title_canon_sha256":"21d2566baf0f5172d7ee19a7316b8aee4142221e51b06566e388709717806642"},"schema_version":"1.0","source":{"id":"2203.06915","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.06915","created_at":"2026-07-05T04:06:03Z"},{"alias_kind":"arxiv_version","alias_value":"2203.06915v2","created_at":"2026-07-05T04:06:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.06915","created_at":"2026-07-05T04:06:03Z"},{"alias_kind":"pith_short_12","alias_value":"6BXBEBGFBPHX","created_at":"2026-07-05T04:06:03Z"},{"alias_kind":"pith_short_16","alias_value":"6BXBEBGFBPHXYOOF","created_at":"2026-07-05T04:06:03Z"},{"alias_kind":"pith_short_8","alias_value":"6BXBEBGF","created_at":"2026-07-05T04:06:03Z"}],"graph_snapshots":[{"event_id":"sha256:27e1bb4f0d4dd7b129b69c9fa7b59cf4d831bad077b9a5e5cac0797ceac4f80b","target":"graph","created_at":"2026-07-05T04:06:03Z","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/2203.06915/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simultaneously considers semantic similarity and instance similarity. In SimMatch, the consistency regularization will be applied on both semantic-level and instance-level. The different augmented views of the same instance are encouraged to have the same class prediction and similar similarity relationship respected to other instances. Next, we instantiated a labeled memory buffer t","authors_text":"Chang Xu, Chen Qian, Fei Wang, Lang Huang, Mingkai Zheng, Shan You","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-14T08:08:48Z","title":"SimMatch: Semi-supervised Learning with Similarity Matching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.06915","kind":"arxiv","version":2},"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:c00f0a5e34852c141fa1c4b6174c9963add74b942196276d674dc5cc34c00902","target":"record","created_at":"2026-07-05T04:06:03Z","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":"595af3e828660cf825dc58897d0991f7301cec0bdbea2b2601e44f18ac2f0573","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-14T08:08:48Z","title_canon_sha256":"21d2566baf0f5172d7ee19a7316b8aee4142221e51b06566e388709717806642"},"schema_version":"1.0","source":{"id":"2203.06915","kind":"arxiv","version":2}},"canonical_sha256":"f06e1204c50bcf7c39c5d0c62904982c7262342531a681ff63d3a55296843cf0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f06e1204c50bcf7c39c5d0c62904982c7262342531a681ff63d3a55296843cf0","first_computed_at":"2026-07-05T04:06:03.003251Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:06:03.003251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cubh0T/bN164yW7E5v79VjYJIo6oqdeaevVGvWr1462k5umykUooO8G21wTZmCIBKr7WAuywl80JvGDTsBq6CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:06:03.003816Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.06915","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c00f0a5e34852c141fa1c4b6174c9963add74b942196276d674dc5cc34c00902","sha256:27e1bb4f0d4dd7b129b69c9fa7b59cf4d831bad077b9a5e5cac0797ceac4f80b"],"state_sha256":"1170b4de4b773c5095b5c4835668bbf74e4be7873bf5da1d216fd00a02af7c99"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M+cYoxWCQKtoSmMxOdCZCfanwhQa5tt9rBg27ZqMWRDrI3jeJ6/48C0FR3qPUmPllRwUOqPyh5F9UccW/zo7AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T13:30:41.168537Z","bundle_sha256":"46a15e57bb77a0fdb4eee7ca8c558c9a7d9a5d7d52d60c85e8d0951d3de92de4"}}