{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:J2EM7SJNL2624XZGLMBFTTS33O","short_pith_number":"pith:J2EM7SJN","canonical_record":{"source":{"id":"1904.12848","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T17:56:59Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV","stat.ML"],"title_canon_sha256":"a707c3349315ce5d7f967ae5ebf566626e7f3c5b525921d556ea59f6843d353a","abstract_canon_sha256":"21ae81e10f562eb02cd41f652e6f66e4b43825633991926ca8aeeaefff3780dd"},"schema_version":"1.0"},"canonical_sha256":"4e88cfc92d5ebdae5f265b0259ce5bdbbcc9ccb96d4b257f069584f0b9381d2c","source":{"kind":"arxiv","id":"1904.12848","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.12848","created_at":"2026-07-05T01:49:13Z"},{"alias_kind":"arxiv_version","alias_value":"1904.12848v6","created_at":"2026-07-05T01:49:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12848","created_at":"2026-07-05T01:49:13Z"},{"alias_kind":"pith_short_12","alias_value":"J2EM7SJNL262","created_at":"2026-07-05T01:49:13Z"},{"alias_kind":"pith_short_16","alias_value":"J2EM7SJNL2624XZG","created_at":"2026-07-05T01:49:13Z"},{"alias_kind":"pith_short_8","alias_value":"J2EM7SJN","created_at":"2026-07-05T01:49:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:J2EM7SJNL2624XZGLMBFTTS33O","target":"record","payload":{"canonical_record":{"source":{"id":"1904.12848","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T17:56:59Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV","stat.ML"],"title_canon_sha256":"a707c3349315ce5d7f967ae5ebf566626e7f3c5b525921d556ea59f6843d353a","abstract_canon_sha256":"21ae81e10f562eb02cd41f652e6f66e4b43825633991926ca8aeeaefff3780dd"},"schema_version":"1.0"},"canonical_sha256":"4e88cfc92d5ebdae5f265b0259ce5bdbbcc9ccb96d4b257f069584f0b9381d2c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:49:13.677207Z","signature_b64":"Y7Va81V1czHiJTM36JNuUvZahv31RrvH8pb8INJIScUYDRG5zHlcsrxDNgNsPLhKdG5I7k4H3KNC5isMytQTDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e88cfc92d5ebdae5f265b0259ce5bdbbcc9ccb96d4b257f069584f0b9381d2c","last_reissued_at":"2026-07-05T01:49:13.676738Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:49:13.676738Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.12848","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:49:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tu8eW11vQ8GietmGMQ69VmSc5zzIKf5zHP4pS9KMLDH9pWVFcqfvXHJR+eNRf+0iMGG1ADW4Q5Db31U1iNEfDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:23:44.881000Z"},"content_sha256":"2b15cf07fc98f002f19635ea148fe5bbdde3c8871072c5431a8b25451572340c","schema_version":"1.0","event_id":"sha256:2b15cf07fc98f002f19635ea148fe5bbdde3c8871072c5431a8b25451572340c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:J2EM7SJNL2624XZGLMBFTTS33O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Data Augmentation for Consistency Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Eduard Hovy, Minh-Thang Luong, Qizhe Xie, Quoc V. Le, Zihang Dai","submitted_at":"2019-04-29T17:56:59Z","abstract_excerpt":"Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12848","kind":"arxiv","version":6},"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/1904.12848/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":"2026-07-05T01:49:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BTEtn560vFhbFHODQVE1rY9U2HDF//+yGi4ZN60qPUsia+Bj9mhjV9r22m0pkygsKoiMvzBDz5elasyNe44kCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:23:44.881501Z"},"content_sha256":"72b96046f5c13c12123e5e62711fb866a1adfb15874378b9e6bbb06ac5fc788a","schema_version":"1.0","event_id":"sha256:72b96046f5c13c12123e5e62711fb866a1adfb15874378b9e6bbb06ac5fc788a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J2EM7SJNL2624XZGLMBFTTS33O/bundle.json","state_url":"https://pith.science/pith/J2EM7SJNL2624XZGLMBFTTS33O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J2EM7SJNL2624XZGLMBFTTS33O/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":"2026-08-06T05:23:44Z","links":{"resolver":"https://pith.science/pith/J2EM7SJNL2624XZGLMBFTTS33O","bundle":"https://pith.science/pith/J2EM7SJNL2624XZGLMBFTTS33O/bundle.json","state":"https://pith.science/pith/J2EM7SJNL2624XZGLMBFTTS33O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J2EM7SJNL2624XZGLMBFTTS33O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:J2EM7SJNL2624XZGLMBFTTS33O","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":"21ae81e10f562eb02cd41f652e6f66e4b43825633991926ca8aeeaefff3780dd","cross_cats_sorted":["cs.AI","cs.CL","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T17:56:59Z","title_canon_sha256":"a707c3349315ce5d7f967ae5ebf566626e7f3c5b525921d556ea59f6843d353a"},"schema_version":"1.0","source":{"id":"1904.12848","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.12848","created_at":"2026-07-05T01:49:13Z"},{"alias_kind":"arxiv_version","alias_value":"1904.12848v6","created_at":"2026-07-05T01:49:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12848","created_at":"2026-07-05T01:49:13Z"},{"alias_kind":"pith_short_12","alias_value":"J2EM7SJNL262","created_at":"2026-07-05T01:49:13Z"},{"alias_kind":"pith_short_16","alias_value":"J2EM7SJNL2624XZG","created_at":"2026-07-05T01:49:13Z"},{"alias_kind":"pith_short_8","alias_value":"J2EM7SJN","created_at":"2026-07-05T01:49:13Z"}],"graph_snapshots":[{"event_id":"sha256:72b96046f5c13c12123e5e62711fb866a1adfb15874378b9e6bbb06ac5fc788a","target":"graph","created_at":"2026-07-05T01:49:13Z","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/1904.12848/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentati","authors_text":"Eduard Hovy, Minh-Thang Luong, Qizhe Xie, Quoc V. Le, Zihang Dai","cross_cats":["cs.AI","cs.CL","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T17:56:59Z","title":"Unsupervised Data Augmentation for Consistency Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12848","kind":"arxiv","version":6},"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":"sha256:2b15cf07fc98f002f19635ea148fe5bbdde3c8871072c5431a8b25451572340c","target":"record","created_at":"2026-07-05T01:49:13Z","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":"21ae81e10f562eb02cd41f652e6f66e4b43825633991926ca8aeeaefff3780dd","cross_cats_sorted":["cs.AI","cs.CL","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T17:56:59Z","title_canon_sha256":"a707c3349315ce5d7f967ae5ebf566626e7f3c5b525921d556ea59f6843d353a"},"schema_version":"1.0","source":{"id":"1904.12848","kind":"arxiv","version":6}},"canonical_sha256":"4e88cfc92d5ebdae5f265b0259ce5bdbbcc9ccb96d4b257f069584f0b9381d2c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4e88cfc92d5ebdae5f265b0259ce5bdbbcc9ccb96d4b257f069584f0b9381d2c","first_computed_at":"2026-07-05T01:49:13.676738Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:49:13.676738Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Y7Va81V1czHiJTM36JNuUvZahv31RrvH8pb8INJIScUYDRG5zHlcsrxDNgNsPLhKdG5I7k4H3KNC5isMytQTDg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:49:13.677207Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.12848","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b15cf07fc98f002f19635ea148fe5bbdde3c8871072c5431a8b25451572340c","sha256:72b96046f5c13c12123e5e62711fb866a1adfb15874378b9e6bbb06ac5fc788a"],"state_sha256":"1a019de3f6eaff80ffeb21cadf7faec75723b7a9da3f6c46b0e68b5be85039cf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SIWS/7YJhzOoQXFHAKsHHAg+fGFbsEKZvE4HGm1KF0p+6f+fvFvpnUdvv936cn/qDbEZomxMVDeJiInKZJkuCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T05:23:44.886119Z","bundle_sha256":"3a21533b5e688d0187232b40b13a32ac997fb94d48fb26a27dc6969246513639"}}