{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ISLTXY4TQHV3B7JJOFXXIGAGME","short_pith_number":"pith:ISLTXY4T","canonical_record":{"source":{"id":"2306.07483","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-13T01:09:18Z","cross_cats_sorted":[],"title_canon_sha256":"c2dc3924cebf39ec2898d680fc31e681f9406fb0a874fce9235e3fbdc3421c30","abstract_canon_sha256":"586df88fa4ac05d5c2aa2a0cc29f8ee0ac1e77f96ae6f4b7a5e413e6bb8ddb2e"},"schema_version":"1.0"},"canonical_sha256":"44973be39381ebb0fd29716f7418066138e63e6a77d9299377b03d286fa4c6bd","source":{"kind":"arxiv","id":"2306.07483","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.07483","created_at":"2026-07-05T06:20:05Z"},{"alias_kind":"arxiv_version","alias_value":"2306.07483v1","created_at":"2026-07-05T06:20:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.07483","created_at":"2026-07-05T06:20:05Z"},{"alias_kind":"pith_short_12","alias_value":"ISLTXY4TQHV3","created_at":"2026-07-05T06:20:05Z"},{"alias_kind":"pith_short_16","alias_value":"ISLTXY4TQHV3B7JJ","created_at":"2026-07-05T06:20:05Z"},{"alias_kind":"pith_short_8","alias_value":"ISLTXY4T","created_at":"2026-07-05T06:20:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ISLTXY4TQHV3B7JJOFXXIGAGME","target":"record","payload":{"canonical_record":{"source":{"id":"2306.07483","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-13T01:09:18Z","cross_cats_sorted":[],"title_canon_sha256":"c2dc3924cebf39ec2898d680fc31e681f9406fb0a874fce9235e3fbdc3421c30","abstract_canon_sha256":"586df88fa4ac05d5c2aa2a0cc29f8ee0ac1e77f96ae6f4b7a5e413e6bb8ddb2e"},"schema_version":"1.0"},"canonical_sha256":"44973be39381ebb0fd29716f7418066138e63e6a77d9299377b03d286fa4c6bd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:20:05.093134Z","signature_b64":"BBEztfgCaemuPpAFR0ndbG/bxxgy0hXfoke55gVb5MIM/Ff7mDg2m2lrqNWto+nh10uqNTYkUqfLOc5i8vPfBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44973be39381ebb0fd29716f7418066138e63e6a77d9299377b03d286fa4c6bd","last_reissued_at":"2026-07-05T06:20:05.092805Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:20:05.092805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.07483","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":"2026-07-05T06:20:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RwF2LAQVuIZv+rGeYTo0dvxZIUIC8e5C0vLN77FkZ+dDA/pWfhWyQKdwNWMpdZR7RQONxiXyF+r/3ReQW983Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T10:05:52.481516Z"},"content_sha256":"22ce6bcd1db46892ee64af05102e2905c2017c71e8378d93b1acefd53d647dfb","schema_version":"1.0","event_id":"sha256:22ce6bcd1db46892ee64af05102e2905c2017c71e8378d93b1acefd53d647dfb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ISLTXY4TQHV3B7JJOFXXIGAGME","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-supervised learning made simple with self-supervised clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Elisa Ricci, Enrico Fini, Julien Mairal, Karteek Alahari, Moin Nabi, Pietro Astolfi, Xavier Alameda-Pineda","submitted_at":"2023-06-13T01:09:18Z","abstract_excerpt":"Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partially available, motivating a recent line of work on semi-supervised methods inspired by self-supervised principles. In this paper, we propose a conceptually simple yet empirically powerful approach to turn clustering-based self-supervised methods such as SwAV or DINO into semi-supervised learners. More precisely, we introduce a multi-task framework merging a supervised objective using ground-truth labels and a self-supe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.07483","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2306.07483/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":"2026-07-05T06:20:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QyHZUX30xOrPz1rzIGHQ+ZR2Hnr57QicD8w3kwhAthUkg9Nb7KaHIwsMaOHXmOoMCOI1SNBWn1g76i9Q/a7IDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T10:05:52.481876Z"},"content_sha256":"ef3466dfcf951f6f81bd7c021b862300bab3863e82358f6ecec7af9ab35fae17","schema_version":"1.0","event_id":"sha256:ef3466dfcf951f6f81bd7c021b862300bab3863e82358f6ecec7af9ab35fae17"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ISLTXY4TQHV3B7JJOFXXIGAGME/bundle.json","state_url":"https://pith.science/pith/ISLTXY4TQHV3B7JJOFXXIGAGME/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ISLTXY4TQHV3B7JJOFXXIGAGME/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":"2026-07-07T10:05:52Z","links":{"resolver":"https://pith.science/pith/ISLTXY4TQHV3B7JJOFXXIGAGME","bundle":"https://pith.science/pith/ISLTXY4TQHV3B7JJOFXXIGAGME/bundle.json","state":"https://pith.science/pith/ISLTXY4TQHV3B7JJOFXXIGAGME/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ISLTXY4TQHV3B7JJOFXXIGAGME/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ISLTXY4TQHV3B7JJOFXXIGAGME","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":"586df88fa4ac05d5c2aa2a0cc29f8ee0ac1e77f96ae6f4b7a5e413e6bb8ddb2e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-13T01:09:18Z","title_canon_sha256":"c2dc3924cebf39ec2898d680fc31e681f9406fb0a874fce9235e3fbdc3421c30"},"schema_version":"1.0","source":{"id":"2306.07483","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.07483","created_at":"2026-07-05T06:20:05Z"},{"alias_kind":"arxiv_version","alias_value":"2306.07483v1","created_at":"2026-07-05T06:20:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.07483","created_at":"2026-07-05T06:20:05Z"},{"alias_kind":"pith_short_12","alias_value":"ISLTXY4TQHV3","created_at":"2026-07-05T06:20:05Z"},{"alias_kind":"pith_short_16","alias_value":"ISLTXY4TQHV3B7JJ","created_at":"2026-07-05T06:20:05Z"},{"alias_kind":"pith_short_8","alias_value":"ISLTXY4T","created_at":"2026-07-05T06:20:05Z"}],"graph_snapshots":[{"event_id":"sha256:ef3466dfcf951f6f81bd7c021b862300bab3863e82358f6ecec7af9ab35fae17","target":"graph","created_at":"2026-07-05T06:20:05Z","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/2306.07483/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partially available, motivating a recent line of work on semi-supervised methods inspired by self-supervised principles. In this paper, we propose a conceptually simple yet empirically powerful approach to turn clustering-based self-supervised methods such as SwAV or DINO into semi-supervised learners. More precisely, we introduce a multi-task framework merging a supervised objective using ground-truth labels and a self-supe","authors_text":"Elisa Ricci, Enrico Fini, Julien Mairal, Karteek Alahari, Moin Nabi, Pietro Astolfi, Xavier Alameda-Pineda","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-13T01:09:18Z","title":"Semi-supervised learning made simple with self-supervised clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.07483","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":"sha256:22ce6bcd1db46892ee64af05102e2905c2017c71e8378d93b1acefd53d647dfb","target":"record","created_at":"2026-07-05T06:20:05Z","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":"586df88fa4ac05d5c2aa2a0cc29f8ee0ac1e77f96ae6f4b7a5e413e6bb8ddb2e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-13T01:09:18Z","title_canon_sha256":"c2dc3924cebf39ec2898d680fc31e681f9406fb0a874fce9235e3fbdc3421c30"},"schema_version":"1.0","source":{"id":"2306.07483","kind":"arxiv","version":1}},"canonical_sha256":"44973be39381ebb0fd29716f7418066138e63e6a77d9299377b03d286fa4c6bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"44973be39381ebb0fd29716f7418066138e63e6a77d9299377b03d286fa4c6bd","first_computed_at":"2026-07-05T06:20:05.092805Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:20:05.092805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BBEztfgCaemuPpAFR0ndbG/bxxgy0hXfoke55gVb5MIM/Ff7mDg2m2lrqNWto+nh10uqNTYkUqfLOc5i8vPfBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:20:05.093134Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.07483","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:22ce6bcd1db46892ee64af05102e2905c2017c71e8378d93b1acefd53d647dfb","sha256:ef3466dfcf951f6f81bd7c021b862300bab3863e82358f6ecec7af9ab35fae17"],"state_sha256":"9578ca2d388e620fdafbb42f89997add7f590af67f189b877d7d0c2449c93d8b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"At+7LR78J/xI9RQxJxSqBR4HPjoATH2H2uQZp4phvM6qq7ReHO+HbIoDKPlLkT5rqU7hP2EuVUoIsOUGn8YBAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T10:05:52.483996Z","bundle_sha256":"2c34902dc054152da13de32fd8837f9ead5e4698d6b638095dbc528903e378d1"}}