{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:NBMC2AEF5E2SJ5EAFZSO2UA5KI","short_pith_number":"pith:NBMC2AEF","canonical_record":{"source":{"id":"2202.11915","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-24T06:00:05Z","cross_cats_sorted":[],"title_canon_sha256":"144d12baa88977bc6fd4212ef4457b5c5757041903e975c5b42b55441f05fe65","abstract_canon_sha256":"f99b2973c2f7887c5e6b7ae4696a5f60f5e30c66f77950490def28b0c3a00c57"},"schema_version":"1.0"},"canonical_sha256":"68582d0085e93524f4802e64ed501d520ca0b687f4d173f2a862d3eb40005153","source":{"kind":"arxiv","id":"2202.11915","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.11915","created_at":"2026-07-05T04:34:03Z"},{"alias_kind":"arxiv_version","alias_value":"2202.11915v2","created_at":"2026-07-05T04:34:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.11915","created_at":"2026-07-05T04:34:03Z"},{"alias_kind":"pith_short_12","alias_value":"NBMC2AEF5E2S","created_at":"2026-07-05T04:34:03Z"},{"alias_kind":"pith_short_16","alias_value":"NBMC2AEF5E2SJ5EA","created_at":"2026-07-05T04:34:03Z"},{"alias_kind":"pith_short_8","alias_value":"NBMC2AEF","created_at":"2026-07-05T04:34:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:NBMC2AEF5E2SJ5EAFZSO2UA5KI","target":"record","payload":{"canonical_record":{"source":{"id":"2202.11915","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-24T06:00:05Z","cross_cats_sorted":[],"title_canon_sha256":"144d12baa88977bc6fd4212ef4457b5c5757041903e975c5b42b55441f05fe65","abstract_canon_sha256":"f99b2973c2f7887c5e6b7ae4696a5f60f5e30c66f77950490def28b0c3a00c57"},"schema_version":"1.0"},"canonical_sha256":"68582d0085e93524f4802e64ed501d520ca0b687f4d173f2a862d3eb40005153","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:34:03.849469Z","signature_b64":"SKc6G4ohnpCwcHyFuLG6Nm/ap7cyGmPS1QK9ZEzICzl6VLKlm/ztOgfr3T+qQFXJlW7i4PiPCNEP9TuWUPkAAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68582d0085e93524f4802e64ed501d520ca0b687f4d173f2a862d3eb40005153","last_reissued_at":"2026-07-05T04:34:03.848973Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:34:03.848973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.11915","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":"2026-07-05T04:34:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WHTrcHHI8lp0eKcNMExPaxuLmgXAVmaOJV5t6KeCFDFtPz6qJX5IL6/vbhy3Na2jmzUZm4pJrgmxqAA9/54ECQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T06:58:42.293278Z"},"content_sha256":"7a14c0e7267bf927dc4dd68e234da8b0ccfc73cf2ff57175d77979654949d69b","schema_version":"1.0","event_id":"sha256:7a14c0e7267bf927dc4dd68e234da8b0ccfc73cf2ff57175d77979654949d69b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:NBMC2AEF5E2SJ5EAFZSO2UA5KI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"En Zhu, Sihang Zhou, Xiaochang Hu, Xihong Yang, Xinwang Liu","submitted_at":"2022-02-24T06:00:05Z","abstract_excerpt":"Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. In the existing literature, consistency regularization-based methods, which force the perturbed samples to have similar predictions with the original ones have attracted much attention for their promising accuracy. However, we observe that, the performance of such methods decreases drastically when the labels get extremely limited, e.g., 2 or 3 labels for each category. Our empirical study finds that the main problem lies with the drifting of semantic information i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.11915","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/2202.11915/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":"2026-07-05T04:34:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M5gtN+8H98NkaXXNVsdY5jvGAJPHaYFsk60gSlxb8zStETlguVWCQBE/tUoKPNHYbGRzDoP0jZRZ+3knQATMDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T06:58:42.293665Z"},"content_sha256":"897bd423d25f3246ab32c5fada40ab38899a697bd76641351394dcd2e0ea7fe0","schema_version":"1.0","event_id":"sha256:897bd423d25f3246ab32c5fada40ab38899a697bd76641351394dcd2e0ea7fe0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NBMC2AEF5E2SJ5EAFZSO2UA5KI/bundle.json","state_url":"https://pith.science/pith/NBMC2AEF5E2SJ5EAFZSO2UA5KI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NBMC2AEF5E2SJ5EAFZSO2UA5KI/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":"2026-07-21T06:58:42Z","links":{"resolver":"https://pith.science/pith/NBMC2AEF5E2SJ5EAFZSO2UA5KI","bundle":"https://pith.science/pith/NBMC2AEF5E2SJ5EAFZSO2UA5KI/bundle.json","state":"https://pith.science/pith/NBMC2AEF5E2SJ5EAFZSO2UA5KI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NBMC2AEF5E2SJ5EAFZSO2UA5KI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:NBMC2AEF5E2SJ5EAFZSO2UA5KI","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":"f99b2973c2f7887c5e6b7ae4696a5f60f5e30c66f77950490def28b0c3a00c57","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-24T06:00:05Z","title_canon_sha256":"144d12baa88977bc6fd4212ef4457b5c5757041903e975c5b42b55441f05fe65"},"schema_version":"1.0","source":{"id":"2202.11915","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.11915","created_at":"2026-07-05T04:34:03Z"},{"alias_kind":"arxiv_version","alias_value":"2202.11915v2","created_at":"2026-07-05T04:34:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.11915","created_at":"2026-07-05T04:34:03Z"},{"alias_kind":"pith_short_12","alias_value":"NBMC2AEF5E2S","created_at":"2026-07-05T04:34:03Z"},{"alias_kind":"pith_short_16","alias_value":"NBMC2AEF5E2SJ5EA","created_at":"2026-07-05T04:34:03Z"},{"alias_kind":"pith_short_8","alias_value":"NBMC2AEF","created_at":"2026-07-05T04:34:03Z"}],"graph_snapshots":[{"event_id":"sha256:897bd423d25f3246ab32c5fada40ab38899a697bd76641351394dcd2e0ea7fe0","target":"graph","created_at":"2026-07-05T04:34:03Z","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/2202.11915/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. In the existing literature, consistency regularization-based methods, which force the perturbed samples to have similar predictions with the original ones have attracted much attention for their promising accuracy. However, we observe that, the performance of such methods decreases drastically when the labels get extremely limited, e.g., 2 or 3 labels for each category. Our empirical study finds that the main problem lies with the drifting of semantic information i","authors_text":"En Zhu, Sihang Zhou, Xiaochang Hu, Xihong Yang, Xinwang Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-24T06:00:05Z","title":"Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.11915","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":"sha256:7a14c0e7267bf927dc4dd68e234da8b0ccfc73cf2ff57175d77979654949d69b","target":"record","created_at":"2026-07-05T04:34:03Z","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":"f99b2973c2f7887c5e6b7ae4696a5f60f5e30c66f77950490def28b0c3a00c57","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-24T06:00:05Z","title_canon_sha256":"144d12baa88977bc6fd4212ef4457b5c5757041903e975c5b42b55441f05fe65"},"schema_version":"1.0","source":{"id":"2202.11915","kind":"arxiv","version":2}},"canonical_sha256":"68582d0085e93524f4802e64ed501d520ca0b687f4d173f2a862d3eb40005153","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"68582d0085e93524f4802e64ed501d520ca0b687f4d173f2a862d3eb40005153","first_computed_at":"2026-07-05T04:34:03.848973Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:34:03.848973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SKc6G4ohnpCwcHyFuLG6Nm/ap7cyGmPS1QK9ZEzICzl6VLKlm/ztOgfr3T+qQFXJlW7i4PiPCNEP9TuWUPkAAA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:34:03.849469Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.11915","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7a14c0e7267bf927dc4dd68e234da8b0ccfc73cf2ff57175d77979654949d69b","sha256:897bd423d25f3246ab32c5fada40ab38899a697bd76641351394dcd2e0ea7fe0"],"state_sha256":"64148913fd80ad87b7d05e960e399e0a8c9a2eed2cb2b1366ac47228c2805100"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L6dr9dwD1V/UQc05QQ1eTe3/lEQjNfsoG8yalOfRE0J1EJ5PikIt1oM5s/kUle5YvKDV+m6yrBSwjIcWpBu8Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-21T06:58:42.295806Z","bundle_sha256":"caf1336fbd4278bfd4479cad38eff2f87dea614f6e35ccad2192d8adbb54d755"}}