{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:UJDAHI2SYVD73RKGLQ6544F6IM","short_pith_number":"pith:UJDAHI2S","canonical_record":{"source":{"id":"2103.00550","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-28T16:22:58Z","cross_cats_sorted":[],"title_canon_sha256":"215cf4b3afda655fc253b5fb80290104c97e4292146aba51d1dec30572b428cb","abstract_canon_sha256":"19ad8cddfa4b887a109cd05a1d051f6e0ed80e6499e888befc9925cb4c345917"},"schema_version":"1.0"},"canonical_sha256":"a24603a352c547fdc5465c3dde70be43389ecb88e43ddbd7af899326a01719c8","source":{"kind":"arxiv","id":"2103.00550","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.00550","created_at":"2026-07-05T07:06:16Z"},{"alias_kind":"arxiv_version","alias_value":"2103.00550v2","created_at":"2026-07-05T07:06:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.00550","created_at":"2026-07-05T07:06:16Z"},{"alias_kind":"pith_short_12","alias_value":"UJDAHI2SYVD7","created_at":"2026-07-05T07:06:16Z"},{"alias_kind":"pith_short_16","alias_value":"UJDAHI2SYVD73RKG","created_at":"2026-07-05T07:06:16Z"},{"alias_kind":"pith_short_8","alias_value":"UJDAHI2S","created_at":"2026-07-05T07:06:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:UJDAHI2SYVD73RKGLQ6544F6IM","target":"record","payload":{"canonical_record":{"source":{"id":"2103.00550","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-28T16:22:58Z","cross_cats_sorted":[],"title_canon_sha256":"215cf4b3afda655fc253b5fb80290104c97e4292146aba51d1dec30572b428cb","abstract_canon_sha256":"19ad8cddfa4b887a109cd05a1d051f6e0ed80e6499e888befc9925cb4c345917"},"schema_version":"1.0"},"canonical_sha256":"a24603a352c547fdc5465c3dde70be43389ecb88e43ddbd7af899326a01719c8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:06:16.863678Z","signature_b64":"bB58gBV7FYsP5sgStTyOAhBzN1tiAtLgXpc+NQNigAtiYFts9F1yi/KWXCcLgqXR2d26ygCsK4C7fRdxjnyYDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a24603a352c547fdc5465c3dde70be43389ecb88e43ddbd7af899326a01719c8","last_reissued_at":"2026-07-05T07:06:16.863177Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:06:16.863177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.00550","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":"2026-07-05T07:06:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EY3PrVIoV+tyXGTguQ3Qm6ufvfErfaz9NLWj1KXIrWdYdU+/zze74MPkGbA6tijryfDnL1GWBIlr07sBj50VDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:35:53.533192Z"},"content_sha256":"daf5225ce8577e04e1bd5268ed552efa4d033c7cecdb6b287940c8642e8506fb","schema_version":"1.0","event_id":"sha256:daf5225ce8577e04e1bd5268ed552efa4d033c7cecdb6b287940c8642e8506fb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:UJDAHI2SYVD73RKGLQ6544F6IM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey on Deep Semi-supervised Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Irwin King, Xiangli Yang, Zenglin Xu, Zixing Song","submitted_at":"2021-02-28T16:22:58Z","abstract_excerpt":"Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design and unsupervised loss functions. We first present a taxonomy for deep semi-supervised learning that categorizes existing methods, including deep generative methods, consistency regularization methods, graph-based methods, pseudo-labeling methods, and hybrid methods. Then we provide a comprehensive review of 52 representative methods and offer a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.00550","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/2103.00550/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":"2026-07-05T07:06:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7JjQDXz0kpPcUcLYRmNbOUx+x22C04/zCJgSV/jrlMrdEogl9naDumY0bF1hu/uEeWDnIZQXJ52AxyRjYFmUCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:35:53.534632Z"},"content_sha256":"abc8e48b39c45a25b28f74e4c69e39850cd3ba0f4a07ca71198a40795c0f4f93","schema_version":"1.0","event_id":"sha256:abc8e48b39c45a25b28f74e4c69e39850cd3ba0f4a07ca71198a40795c0f4f93"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UJDAHI2SYVD73RKGLQ6544F6IM/bundle.json","state_url":"https://pith.science/pith/UJDAHI2SYVD73RKGLQ6544F6IM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UJDAHI2SYVD73RKGLQ6544F6IM/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":"2026-08-07T19:35:53Z","links":{"resolver":"https://pith.science/pith/UJDAHI2SYVD73RKGLQ6544F6IM","bundle":"https://pith.science/pith/UJDAHI2SYVD73RKGLQ6544F6IM/bundle.json","state":"https://pith.science/pith/UJDAHI2SYVD73RKGLQ6544F6IM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UJDAHI2SYVD73RKGLQ6544F6IM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:UJDAHI2SYVD73RKGLQ6544F6IM","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":"19ad8cddfa4b887a109cd05a1d051f6e0ed80e6499e888befc9925cb4c345917","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-28T16:22:58Z","title_canon_sha256":"215cf4b3afda655fc253b5fb80290104c97e4292146aba51d1dec30572b428cb"},"schema_version":"1.0","source":{"id":"2103.00550","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.00550","created_at":"2026-07-05T07:06:16Z"},{"alias_kind":"arxiv_version","alias_value":"2103.00550v2","created_at":"2026-07-05T07:06:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.00550","created_at":"2026-07-05T07:06:16Z"},{"alias_kind":"pith_short_12","alias_value":"UJDAHI2SYVD7","created_at":"2026-07-05T07:06:16Z"},{"alias_kind":"pith_short_16","alias_value":"UJDAHI2SYVD73RKG","created_at":"2026-07-05T07:06:16Z"},{"alias_kind":"pith_short_8","alias_value":"UJDAHI2S","created_at":"2026-07-05T07:06:16Z"}],"graph_snapshots":[{"event_id":"sha256:abc8e48b39c45a25b28f74e4c69e39850cd3ba0f4a07ca71198a40795c0f4f93","target":"graph","created_at":"2026-07-05T07:06:16Z","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/2103.00550/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design and unsupervised loss functions. We first present a taxonomy for deep semi-supervised learning that categorizes existing methods, including deep generative methods, consistency regularization methods, graph-based methods, pseudo-labeling methods, and hybrid methods. Then we provide a comprehensive review of 52 representative methods and offer a ","authors_text":"Irwin King, Xiangli Yang, Zenglin Xu, Zixing Song","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-28T16:22:58Z","title":"A Survey on Deep Semi-supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.00550","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:daf5225ce8577e04e1bd5268ed552efa4d033c7cecdb6b287940c8642e8506fb","target":"record","created_at":"2026-07-05T07:06:16Z","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":"19ad8cddfa4b887a109cd05a1d051f6e0ed80e6499e888befc9925cb4c345917","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-28T16:22:58Z","title_canon_sha256":"215cf4b3afda655fc253b5fb80290104c97e4292146aba51d1dec30572b428cb"},"schema_version":"1.0","source":{"id":"2103.00550","kind":"arxiv","version":2}},"canonical_sha256":"a24603a352c547fdc5465c3dde70be43389ecb88e43ddbd7af899326a01719c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a24603a352c547fdc5465c3dde70be43389ecb88e43ddbd7af899326a01719c8","first_computed_at":"2026-07-05T07:06:16.863177Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:06:16.863177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bB58gBV7FYsP5sgStTyOAhBzN1tiAtLgXpc+NQNigAtiYFts9F1yi/KWXCcLgqXR2d26ygCsK4C7fRdxjnyYDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:06:16.863678Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.00550","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:daf5225ce8577e04e1bd5268ed552efa4d033c7cecdb6b287940c8642e8506fb","sha256:abc8e48b39c45a25b28f74e4c69e39850cd3ba0f4a07ca71198a40795c0f4f93"],"state_sha256":"f232743638a04488a4e7e317b7ec7979aa8f3b0070c84b8b34d13a6932b5d39e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JN5foYnNT1eKIASXveAtwTBEyxOTTIfc9Gr0jNxyMLWs/eyyMeORShzEb6sB+8CPqxxCKnqEmBSS4px7YWqtAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T19:35:53.542523Z","bundle_sha256":"fabb4e4e997fe1d8677829165c3a89c7cf6479e4642f435156a05822f2c21322"}}