{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:LLQHMVSOW2ZBQR5LWOVD7IG5JT","short_pith_number":"pith:LLQHMVSO","canonical_record":{"source":{"id":"1912.02781","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-05T18:18:10Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"f2b7f5c65a7deff724640bee5c59950ed9563367c1948fc7d58f6119dacb464f","abstract_canon_sha256":"f047dc5e9c7bf2e30bccfd538fb7106c1c8430d17f57b12a5f48f9bbf51d25ef"},"schema_version":"1.0"},"canonical_sha256":"5ae076564eb6b21847abb3aa3fa0dd4cec4b284192d8b0b8a2436756cc32efbd","source":{"kind":"arxiv","id":"1912.02781","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.02781","created_at":"2026-07-05T00:41:01Z"},{"alias_kind":"arxiv_version","alias_value":"1912.02781v2","created_at":"2026-07-05T00:41:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.02781","created_at":"2026-07-05T00:41:01Z"},{"alias_kind":"pith_short_12","alias_value":"LLQHMVSOW2ZB","created_at":"2026-07-05T00:41:01Z"},{"alias_kind":"pith_short_16","alias_value":"LLQHMVSOW2ZBQR5L","created_at":"2026-07-05T00:41:01Z"},{"alias_kind":"pith_short_8","alias_value":"LLQHMVSO","created_at":"2026-07-05T00:41:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:LLQHMVSOW2ZBQR5LWOVD7IG5JT","target":"record","payload":{"canonical_record":{"source":{"id":"1912.02781","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-05T18:18:10Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"f2b7f5c65a7deff724640bee5c59950ed9563367c1948fc7d58f6119dacb464f","abstract_canon_sha256":"f047dc5e9c7bf2e30bccfd538fb7106c1c8430d17f57b12a5f48f9bbf51d25ef"},"schema_version":"1.0"},"canonical_sha256":"5ae076564eb6b21847abb3aa3fa0dd4cec4b284192d8b0b8a2436756cc32efbd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:41:01.283329Z","signature_b64":"RkxQAO7ak4SpceHL96TEyArLTwAFk4IHYo7P1UO41zJJgt/MX7ChtAQaJzrbFTmopZoXG+iFIEtBKNFepfk1Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ae076564eb6b21847abb3aa3fa0dd4cec4b284192d8b0b8a2436756cc32efbd","last_reissued_at":"2026-07-05T00:41:01.282840Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:41:01.282840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1912.02781","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":"2026-07-05T00:41:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fhWoeGl9+CrbdmSyiCk2x3g2aZwQ+YLgswDtulYpNS09scxyshJK53CjgvO2nbMJ562jfSdiNC9eAwypYbV2DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T07:13:06.897670Z"},"content_sha256":"33bff660a2f355003547c778cf6e34ea1b2231489ee852fdc044b3b946e6ea76","schema_version":"1.0","event_id":"sha256:33bff660a2f355003547c778cf6e34ea1b2231489ee852fdc044b3b946e6ea76"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:LLQHMVSOW2ZBQR5LWOVD7IG5JT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Balaji Lakshminarayanan, Barret Zoph, Dan Hendrycks, Ekin D. Cubuk, Justin Gilmer, Norman Mu","submitted_at":"2019-12-05T18:18:10Z","abstract_excerpt":"Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AugMix, a data processing technique that is simple to implement, adds limited computational ov"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.02781","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/1912.02781/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":"2026-07-05T00:41:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"feDRR3MRze3FueouL3++XDZ4TngX7RJvGXf8RM7/y9qVYZ+e3X6KZATxiDZC9iTMmEV1UAY2yf72CQilLqipCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T07:13:06.898183Z"},"content_sha256":"3a6df5eff9c6864d5aff8eba4688bfd12e6453c618117908197ae4624ba645f0","schema_version":"1.0","event_id":"sha256:3a6df5eff9c6864d5aff8eba4688bfd12e6453c618117908197ae4624ba645f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LLQHMVSOW2ZBQR5LWOVD7IG5JT/bundle.json","state_url":"https://pith.science/pith/LLQHMVSOW2ZBQR5LWOVD7IG5JT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LLQHMVSOW2ZBQR5LWOVD7IG5JT/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":"2026-08-05T07:13:06Z","links":{"resolver":"https://pith.science/pith/LLQHMVSOW2ZBQR5LWOVD7IG5JT","bundle":"https://pith.science/pith/LLQHMVSOW2ZBQR5LWOVD7IG5JT/bundle.json","state":"https://pith.science/pith/LLQHMVSOW2ZBQR5LWOVD7IG5JT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LLQHMVSOW2ZBQR5LWOVD7IG5JT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:LLQHMVSOW2ZBQR5LWOVD7IG5JT","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":"f047dc5e9c7bf2e30bccfd538fb7106c1c8430d17f57b12a5f48f9bbf51d25ef","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-05T18:18:10Z","title_canon_sha256":"f2b7f5c65a7deff724640bee5c59950ed9563367c1948fc7d58f6119dacb464f"},"schema_version":"1.0","source":{"id":"1912.02781","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.02781","created_at":"2026-07-05T00:41:01Z"},{"alias_kind":"arxiv_version","alias_value":"1912.02781v2","created_at":"2026-07-05T00:41:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.02781","created_at":"2026-07-05T00:41:01Z"},{"alias_kind":"pith_short_12","alias_value":"LLQHMVSOW2ZB","created_at":"2026-07-05T00:41:01Z"},{"alias_kind":"pith_short_16","alias_value":"LLQHMVSOW2ZBQR5L","created_at":"2026-07-05T00:41:01Z"},{"alias_kind":"pith_short_8","alias_value":"LLQHMVSO","created_at":"2026-07-05T00:41:01Z"}],"graph_snapshots":[{"event_id":"sha256:3a6df5eff9c6864d5aff8eba4688bfd12e6453c618117908197ae4624ba645f0","target":"graph","created_at":"2026-07-05T00:41:01Z","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/1912.02781/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AugMix, a data processing technique that is simple to implement, adds limited computational ov","authors_text":"Balaji Lakshminarayanan, Barret Zoph, Dan Hendrycks, Ekin D. Cubuk, Justin Gilmer, Norman Mu","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-05T18:18:10Z","title":"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.02781","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":"sha256:33bff660a2f355003547c778cf6e34ea1b2231489ee852fdc044b3b946e6ea76","target":"record","created_at":"2026-07-05T00:41:01Z","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":"f047dc5e9c7bf2e30bccfd538fb7106c1c8430d17f57b12a5f48f9bbf51d25ef","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-05T18:18:10Z","title_canon_sha256":"f2b7f5c65a7deff724640bee5c59950ed9563367c1948fc7d58f6119dacb464f"},"schema_version":"1.0","source":{"id":"1912.02781","kind":"arxiv","version":2}},"canonical_sha256":"5ae076564eb6b21847abb3aa3fa0dd4cec4b284192d8b0b8a2436756cc32efbd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ae076564eb6b21847abb3aa3fa0dd4cec4b284192d8b0b8a2436756cc32efbd","first_computed_at":"2026-07-05T00:41:01.282840Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:41:01.282840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RkxQAO7ak4SpceHL96TEyArLTwAFk4IHYo7P1UO41zJJgt/MX7ChtAQaJzrbFTmopZoXG+iFIEtBKNFepfk1Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:41:01.283329Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.02781","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:33bff660a2f355003547c778cf6e34ea1b2231489ee852fdc044b3b946e6ea76","sha256:3a6df5eff9c6864d5aff8eba4688bfd12e6453c618117908197ae4624ba645f0"],"state_sha256":"2fe17519dc1e324b8de2e0d4816c56e5a50944c5abbe8f4682f1694bed01d948"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kErSAbTNuYoYAprgONX7G68jqZ5snmAQn/3q0ynmjlNFcFV4oiIdJPMb7c64vKZHjdhonIoX1cfhgBKFWlR8Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T07:13:06.901550Z","bundle_sha256":"20b8251fda613e03e66aef16651cab3ba71da4d68467d68e1e3ee6147f8dbc7f"}}