{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CN7PJMSC6ND6LTLA74YIIYUC2K","short_pith_number":"pith:CN7PJMSC","schema_version":"1.0","canonical_sha256":"137ef4b242f347e5cd60ff30846282d2a9e32ae0c56f27ea887f5a04d1c3c8c4","source":{"kind":"arxiv","id":"1906.12340","version":2},"attestation_state":"computed","paper":{"title":"Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dan Hendrycks, Dawn Song, Mantas Mazeika, Saurav Kadavath","submitted_at":"2019-06-28T17:44:00Z","abstract_excerpt":"Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating or reducing the need for annotations. We find that self-supervision can benefit robustness in a variety of ways, including robustness to adversarial examples, label corruption, and common input corruptions. Additionally, self-supervision greatly benefits out-of-distribution detection on difficult, near-distribution outliers, so much so that it exceeds the performance of fully su"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1906.12340","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-28T17:44:00Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"885effe05368371d375ca16a9930a5a91bd8839b953c93b19df698747fefbec2","abstract_canon_sha256":"5c4711b0ea3ea12357b56aa9b988f003ef8aa93c6de1e8dceea6bd91de3bd861"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:15:38.140282Z","signature_b64":"dfWoawJYbDVO8xkMSEAuLwExqpG1huam5o+707IoCPhAa89+KcTL0h3piRKJS3z2IegprNpQ2rEvMbmtq/G+CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"137ef4b242f347e5cd60ff30846282d2a9e32ae0c56f27ea887f5a04d1c3c8c4","last_reissued_at":"2026-07-05T00:15:38.139828Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:15:38.139828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dan Hendrycks, Dawn Song, Mantas Mazeika, Saurav Kadavath","submitted_at":"2019-06-28T17:44:00Z","abstract_excerpt":"Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating or reducing the need for annotations. We find that self-supervision can benefit robustness in a variety of ways, including robustness to adversarial examples, label corruption, and common input corruptions. Additionally, self-supervision greatly benefits out-of-distribution detection on difficult, near-distribution outliers, so much so that it exceeds the performance of fully su"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.12340","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/1906.12340/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1906.12340","created_at":"2026-07-05T00:15:38.139896+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.12340v2","created_at":"2026-07-05T00:15:38.139896+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.12340","created_at":"2026-07-05T00:15:38.139896+00:00"},{"alias_kind":"pith_short_12","alias_value":"CN7PJMSC6ND6","created_at":"2026-07-05T00:15:38.139896+00:00"},{"alias_kind":"pith_short_16","alias_value":"CN7PJMSC6ND6LTLA","created_at":"2026-07-05T00:15:38.139896+00:00"},{"alias_kind":"pith_short_8","alias_value":"CN7PJMSC","created_at":"2026-07-05T00:15:38.139896+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07689","citing_title":"Agent Delivery Engineering Predictive Reliability Framework","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2606.09881","citing_title":"Toward Calibrated, Fair, and accurate Deepfake Detection","ref_index":298,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CN7PJMSC6ND6LTLA74YIIYUC2K","json":"https://pith.science/pith/CN7PJMSC6ND6LTLA74YIIYUC2K.json","graph_json":"https://pith.science/api/pith-number/CN7PJMSC6ND6LTLA74YIIYUC2K/graph.json","events_json":"https://pith.science/api/pith-number/CN7PJMSC6ND6LTLA74YIIYUC2K/events.json","paper":"https://pith.science/paper/CN7PJMSC"},"agent_actions":{"view_html":"https://pith.science/pith/CN7PJMSC6ND6LTLA74YIIYUC2K","download_json":"https://pith.science/pith/CN7PJMSC6ND6LTLA74YIIYUC2K.json","view_paper":"https://pith.science/paper/CN7PJMSC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.12340&json=true","fetch_graph":"https://pith.science/api/pith-number/CN7PJMSC6ND6LTLA74YIIYUC2K/graph.json","fetch_events":"https://pith.science/api/pith-number/CN7PJMSC6ND6LTLA74YIIYUC2K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CN7PJMSC6ND6LTLA74YIIYUC2K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CN7PJMSC6ND6LTLA74YIIYUC2K/action/storage_attestation","attest_author":"https://pith.science/pith/CN7PJMSC6ND6LTLA74YIIYUC2K/action/author_attestation","sign_citation":"https://pith.science/pith/CN7PJMSC6ND6LTLA74YIIYUC2K/action/citation_signature","submit_replication":"https://pith.science/pith/CN7PJMSC6ND6LTLA74YIIYUC2K/action/replication_record"}},"created_at":"2026-07-05T00:15:38.139896+00:00","updated_at":"2026-07-05T00:15:38.139896+00:00"}