{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:DBLRCH47R6CMS5TDRXAB5B7XBJ","short_pith_number":"pith:DBLRCH47","schema_version":"1.0","canonical_sha256":"1857111f9f8f84c976638dc01e87f70a6a82e8dd66a2adaab310b1fc59a8f743","source":{"kind":"arxiv","id":"2110.09327","version":1},"attestation_state":"computed","paper":{"title":"Self-Supervised Representation Learning: Introduction, Advances and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chen Change Loy, Henry Gouk, Linus Ericsson, Timothy M. Hospedales","submitted_at":"2021-10-18T13:51:22Z","abstract_excerpt":"Self-supervised representation learning methods aim to provide powerful deep feature learning without the requirement of large annotated datasets, thus alleviating the annotation bottleneck that is one of the main barriers to practical deployment of deep learning today. These methods have advanced rapidly in recent years, with their efficacy approaching and sometimes surpassing fully supervised pre-training alternatives across a variety of data modalities including image, video, sound, text and graphs. This article introduces this vibrant area including key concepts, the four main families of "},"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":"2110.09327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-18T13:51:22Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"e38dbe4dba98eec4e225ff4fa2db72503cddb7a944b16385666de27e5a6a4f59","abstract_canon_sha256":"a5f267eef5629da7b60482ac9de7e530f85642bbb6aeca9e3440587fc7ed3b9b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:23:38.485183Z","signature_b64":"8i3ney+BtOSsU7hCntCeKQUdtGSId4sdSDPchA0emqTquCOBjeSY3VXNnHFZHcFB2q6/5iDfYpKlV3YYRT1fDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1857111f9f8f84c976638dc01e87f70a6a82e8dd66a2adaab310b1fc59a8f743","last_reissued_at":"2026-07-05T04:23:38.484711Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:23:38.484711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Representation Learning: Introduction, Advances and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chen Change Loy, Henry Gouk, Linus Ericsson, Timothy M. Hospedales","submitted_at":"2021-10-18T13:51:22Z","abstract_excerpt":"Self-supervised representation learning methods aim to provide powerful deep feature learning without the requirement of large annotated datasets, thus alleviating the annotation bottleneck that is one of the main barriers to practical deployment of deep learning today. These methods have advanced rapidly in recent years, with their efficacy approaching and sometimes surpassing fully supervised pre-training alternatives across a variety of data modalities including image, video, sound, text and graphs. This article introduces this vibrant area including key concepts, the four main families of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.09327","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/2110.09327/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":"2110.09327","created_at":"2026-07-05T04:23:38.484770+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.09327v1","created_at":"2026-07-05T04:23:38.484770+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.09327","created_at":"2026-07-05T04:23:38.484770+00:00"},{"alias_kind":"pith_short_12","alias_value":"DBLRCH47R6CM","created_at":"2026-07-05T04:23:38.484770+00:00"},{"alias_kind":"pith_short_16","alias_value":"DBLRCH47R6CMS5TD","created_at":"2026-07-05T04:23:38.484770+00:00"},{"alias_kind":"pith_short_8","alias_value":"DBLRCH47","created_at":"2026-07-05T04:23:38.484770+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DBLRCH47R6CMS5TDRXAB5B7XBJ","json":"https://pith.science/pith/DBLRCH47R6CMS5TDRXAB5B7XBJ.json","graph_json":"https://pith.science/api/pith-number/DBLRCH47R6CMS5TDRXAB5B7XBJ/graph.json","events_json":"https://pith.science/api/pith-number/DBLRCH47R6CMS5TDRXAB5B7XBJ/events.json","paper":"https://pith.science/paper/DBLRCH47"},"agent_actions":{"view_html":"https://pith.science/pith/DBLRCH47R6CMS5TDRXAB5B7XBJ","download_json":"https://pith.science/pith/DBLRCH47R6CMS5TDRXAB5B7XBJ.json","view_paper":"https://pith.science/paper/DBLRCH47","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.09327&json=true","fetch_graph":"https://pith.science/api/pith-number/DBLRCH47R6CMS5TDRXAB5B7XBJ/graph.json","fetch_events":"https://pith.science/api/pith-number/DBLRCH47R6CMS5TDRXAB5B7XBJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DBLRCH47R6CMS5TDRXAB5B7XBJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DBLRCH47R6CMS5TDRXAB5B7XBJ/action/storage_attestation","attest_author":"https://pith.science/pith/DBLRCH47R6CMS5TDRXAB5B7XBJ/action/author_attestation","sign_citation":"https://pith.science/pith/DBLRCH47R6CMS5TDRXAB5B7XBJ/action/citation_signature","submit_replication":"https://pith.science/pith/DBLRCH47R6CMS5TDRXAB5B7XBJ/action/replication_record"}},"created_at":"2026-07-05T04:23:38.484770+00:00","updated_at":"2026-07-05T04:23:38.484770+00:00"}