{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:GGJVDBS43GUCX2PXYIJUMSUC44","short_pith_number":"pith:GGJVDBS4","canonical_record":{"source":{"id":"1911.09785","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-21T23:44:25Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"fe4068c72b53ba1eae774bbfc0735e4b67ce14773609a6f43da6e469e509b027","abstract_canon_sha256":"468e1362b424c5a8bf61e87d4fe10d5abfc970e1330b679e46fec6375c7532c1"},"schema_version":"1.0"},"canonical_sha256":"319351865cd9a82be9f7c213464a82e72c165784ae7e555664fe1819b1617d44","source":{"kind":"arxiv","id":"1911.09785","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.09785","created_at":"2026-07-05T00:40:35Z"},{"alias_kind":"arxiv_version","alias_value":"1911.09785v2","created_at":"2026-07-05T00:40:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.09785","created_at":"2026-07-05T00:40:35Z"},{"alias_kind":"pith_short_12","alias_value":"GGJVDBS43GUC","created_at":"2026-07-05T00:40:35Z"},{"alias_kind":"pith_short_16","alias_value":"GGJVDBS43GUCX2PX","created_at":"2026-07-05T00:40:35Z"},{"alias_kind":"pith_short_8","alias_value":"GGJVDBS4","created_at":"2026-07-05T00:40:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:GGJVDBS43GUCX2PXYIJUMSUC44","target":"record","payload":{"canonical_record":{"source":{"id":"1911.09785","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-21T23:44:25Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"fe4068c72b53ba1eae774bbfc0735e4b67ce14773609a6f43da6e469e509b027","abstract_canon_sha256":"468e1362b424c5a8bf61e87d4fe10d5abfc970e1330b679e46fec6375c7532c1"},"schema_version":"1.0"},"canonical_sha256":"319351865cd9a82be9f7c213464a82e72c165784ae7e555664fe1819b1617d44","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:40:35.131083Z","signature_b64":"/mZkZWBLBX2ioDz7tgmrDxjvHxSHyJX2QVYfkeD7qg0CKmuEW700O9+DEjmrLvEnoCODXG9qFeWtV7+KwK1eCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"319351865cd9a82be9f7c213464a82e72c165784ae7e555664fe1819b1617d44","last_reissued_at":"2026-07-05T00:40:35.130598Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:40:35.130598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1911.09785","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":"2026-07-05T00:40:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gn5KCPu89noXReWVMGjacwWS+k762kHtgqlVttFTdN7XQvhyvRv8KhdEuhIVsoH1PIbH7yt5m3l5psaxcItVAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:42:11.139773Z"},"content_sha256":"def177d4e325d8f1494e41edcf142a8668c5edcc65e1641c28252acf94712314","schema_version":"1.0","event_id":"sha256:def177d4e325d8f1494e41edcf142a8668c5edcc65e1641c28252acf94712314"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:GGJVDBS43GUCX2PXYIJUMSUC44","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alex Kurakin, Colin Raffel, David Berthelot, Ekin D. Cubuk, Han Zhang, Kihyuk Sohn, Nicholas Carlini","submitted_at":"2019-11-21T23:44:25Z","abstract_excerpt":"We improve the recently-proposed \"MixMatch\" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels. Augmentation anchoring feeds multiple strongly augmented versions of an input into the model and encourages each output to be close to the prediction for a weakly-augmented version of the same input. To produce strong augmentations, we propose a variant of AutoAugment which le"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.09785","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/1911.09785/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":"2026-07-05T00:40:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dnhnOOD+FAsKdvqMG0ptO1mzvTQDnBC752vGych1r7sHP+4BnqPuQHUDCx+ZXaM04CCTpLCZeX7PokvZtqGBDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:42:11.140367Z"},"content_sha256":"b3cf9c2029833d4726b1a591dec59fdf3cc5d8bd2394a24f91031c2d218fe81d","schema_version":"1.0","event_id":"sha256:b3cf9c2029833d4726b1a591dec59fdf3cc5d8bd2394a24f91031c2d218fe81d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GGJVDBS43GUCX2PXYIJUMSUC44/bundle.json","state_url":"https://pith.science/pith/GGJVDBS43GUCX2PXYIJUMSUC44/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GGJVDBS43GUCX2PXYIJUMSUC44/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":"2026-08-07T03:42:11Z","links":{"resolver":"https://pith.science/pith/GGJVDBS43GUCX2PXYIJUMSUC44","bundle":"https://pith.science/pith/GGJVDBS43GUCX2PXYIJUMSUC44/bundle.json","state":"https://pith.science/pith/GGJVDBS43GUCX2PXYIJUMSUC44/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GGJVDBS43GUCX2PXYIJUMSUC44/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:GGJVDBS43GUCX2PXYIJUMSUC44","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":"468e1362b424c5a8bf61e87d4fe10d5abfc970e1330b679e46fec6375c7532c1","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-21T23:44:25Z","title_canon_sha256":"fe4068c72b53ba1eae774bbfc0735e4b67ce14773609a6f43da6e469e509b027"},"schema_version":"1.0","source":{"id":"1911.09785","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.09785","created_at":"2026-07-05T00:40:35Z"},{"alias_kind":"arxiv_version","alias_value":"1911.09785v2","created_at":"2026-07-05T00:40:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.09785","created_at":"2026-07-05T00:40:35Z"},{"alias_kind":"pith_short_12","alias_value":"GGJVDBS43GUC","created_at":"2026-07-05T00:40:35Z"},{"alias_kind":"pith_short_16","alias_value":"GGJVDBS43GUCX2PX","created_at":"2026-07-05T00:40:35Z"},{"alias_kind":"pith_short_8","alias_value":"GGJVDBS4","created_at":"2026-07-05T00:40:35Z"}],"graph_snapshots":[{"event_id":"sha256:b3cf9c2029833d4726b1a591dec59fdf3cc5d8bd2394a24f91031c2d218fe81d","target":"graph","created_at":"2026-07-05T00:40:35Z","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/1911.09785/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We improve the recently-proposed \"MixMatch\" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels. Augmentation anchoring feeds multiple strongly augmented versions of an input into the model and encourages each output to be close to the prediction for a weakly-augmented version of the same input. To produce strong augmentations, we propose a variant of AutoAugment which le","authors_text":"Alex Kurakin, Colin Raffel, David Berthelot, Ekin D. Cubuk, Han Zhang, Kihyuk Sohn, Nicholas Carlini","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.09785","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:def177d4e325d8f1494e41edcf142a8668c5edcc65e1641c28252acf94712314","target":"record","created_at":"2026-07-05T00:40:35Z","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":"468e1362b424c5a8bf61e87d4fe10d5abfc970e1330b679e46fec6375c7532c1","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-21T23:44:25Z","title_canon_sha256":"fe4068c72b53ba1eae774bbfc0735e4b67ce14773609a6f43da6e469e509b027"},"schema_version":"1.0","source":{"id":"1911.09785","kind":"arxiv","version":2}},"canonical_sha256":"319351865cd9a82be9f7c213464a82e72c165784ae7e555664fe1819b1617d44","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"319351865cd9a82be9f7c213464a82e72c165784ae7e555664fe1819b1617d44","first_computed_at":"2026-07-05T00:40:35.130598Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:40:35.130598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/mZkZWBLBX2ioDz7tgmrDxjvHxSHyJX2QVYfkeD7qg0CKmuEW700O9+DEjmrLvEnoCODXG9qFeWtV7+KwK1eCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:40:35.131083Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.09785","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:def177d4e325d8f1494e41edcf142a8668c5edcc65e1641c28252acf94712314","sha256:b3cf9c2029833d4726b1a591dec59fdf3cc5d8bd2394a24f91031c2d218fe81d"],"state_sha256":"3c14df1514c68fb06160748f7735dee922122192f4bf1b212dec5ca3ecf1f1f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ycg4Yjm92YQCsu6JPfG687oPI6AlJljX/dDH0HFcL1fpNs7IrpygI5T+VJTFSUmrw4qUFaDf7ZjHzxxd0U9DDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T03:42:11.144410Z","bundle_sha256":"4db234950fdc6e8a8695f44f55f36ceae07b6697e8290b8eb304d4a123dbe68b"}}