{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:I7MBTB6YJFVCSVAISK62SFHYGA","short_pith_number":"pith:I7MBTB6Y","canonical_record":{"source":{"id":"2110.11205","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T15:30:40Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"d1221068e9f359294e8595188a5177b7d81837b9fcc523c91ad7ad3867a63888","abstract_canon_sha256":"9f229db14e4446a3f09f29dbc26b0af0ebf09535feeac55b6b3c3a4d98bfece1"},"schema_version":"1.0"},"canonical_sha256":"47d81987d8496a29540892bda914f83010d04faf768fd6d12b79882c5560141b","source":{"kind":"arxiv","id":"2110.11205","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.11205","created_at":"2026-07-05T05:35:11Z"},{"alias_kind":"arxiv_version","alias_value":"2110.11205v3","created_at":"2026-07-05T05:35:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.11205","created_at":"2026-07-05T05:35:11Z"},{"alias_kind":"pith_short_12","alias_value":"I7MBTB6YJFVC","created_at":"2026-07-05T05:35:11Z"},{"alias_kind":"pith_short_16","alias_value":"I7MBTB6YJFVCSVAI","created_at":"2026-07-05T05:35:11Z"},{"alias_kind":"pith_short_8","alias_value":"I7MBTB6Y","created_at":"2026-07-05T05:35:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:I7MBTB6YJFVCSVAISK62SFHYGA","target":"record","payload":{"canonical_record":{"source":{"id":"2110.11205","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T15:30:40Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"d1221068e9f359294e8595188a5177b7d81837b9fcc523c91ad7ad3867a63888","abstract_canon_sha256":"9f229db14e4446a3f09f29dbc26b0af0ebf09535feeac55b6b3c3a4d98bfece1"},"schema_version":"1.0"},"canonical_sha256":"47d81987d8496a29540892bda914f83010d04faf768fd6d12b79882c5560141b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:35:11.829180Z","signature_b64":"S2XMbsIbBKCszuxwXLFPRICDsGY095Z2cR2XmvAjw5WGJnf7gMn4gPFO6Elj1yHxR2zYYs5GgXF5yFxnFr5CBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47d81987d8496a29540892bda914f83010d04faf768fd6d12b79882c5560141b","last_reissued_at":"2026-07-05T05:35:11.828545Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:35:11.828545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.11205","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:35:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xx29h9t6rqBNLYc9kDuV8e0AR+C1JZ3XOWaFoPgp/dWBSLj8jX2JiKucSTfui70G2tYYCyDl4UqX57oFsUCsCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:03:24.880138Z"},"content_sha256":"7983ae13dfb5f5ab298ae1aa1aca14dec69ebe1b3800bd47fbe6b6a7bd865706","schema_version":"1.0","event_id":"sha256:7983ae13dfb5f5ab298ae1aa1aca14dec69ebe1b3800bd47fbe6b6a7bd865706"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:I7MBTB6YJFVCSVAISK62SFHYGA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robustness through Data Augmentation Loss Consistency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV"],"primary_cat":"cs.LG","authors_text":"Ahmad Beirami, Alborz Geramifard, Chinnadhurai Sankar, Meisam Razaviyayn, Pooyan Amini, Satwik Kottur, Shaunak Halbe, Tianjian Huang","submitted_at":"2021-10-21T15:30:40Z","abstract_excerpt":"While deep learning through empirical risk minimization (ERM) has succeeded at achieving human-level performance at a variety of complex tasks, ERM is not robust to distribution shifts or adversarial attacks. Synthetic data augmentation followed by empirical risk minimization (DA-ERM) is a simple and widely used solution to improve robustness in ERM. In addition, consistency regularization can be applied to further improve the robustness of the model by forcing the representation of the original sample and the augmented one to be similar. However, existing consistency regularization methods ar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.11205","kind":"arxiv","version":3},"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/2110.11205/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":"2026-07-05T05:35:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QmcjQzArVyhnb7jNnE3dwa/1n4/vtmZDJE5EB7Cx+Z23nb1UtqyFxqR7jiwN7mkPwRV8YOqZi+qbrXTPYbBIDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:03:24.880650Z"},"content_sha256":"c60829b893529197cee3d427f0ba1cca455b857501344c4bd866bd31877881a7","schema_version":"1.0","event_id":"sha256:c60829b893529197cee3d427f0ba1cca455b857501344c4bd866bd31877881a7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I7MBTB6YJFVCSVAISK62SFHYGA/bundle.json","state_url":"https://pith.science/pith/I7MBTB6YJFVCSVAISK62SFHYGA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I7MBTB6YJFVCSVAISK62SFHYGA/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":"2026-08-05T03:03:24Z","links":{"resolver":"https://pith.science/pith/I7MBTB6YJFVCSVAISK62SFHYGA","bundle":"https://pith.science/pith/I7MBTB6YJFVCSVAISK62SFHYGA/bundle.json","state":"https://pith.science/pith/I7MBTB6YJFVCSVAISK62SFHYGA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I7MBTB6YJFVCSVAISK62SFHYGA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:I7MBTB6YJFVCSVAISK62SFHYGA","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":"9f229db14e4446a3f09f29dbc26b0af0ebf09535feeac55b6b3c3a4d98bfece1","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T15:30:40Z","title_canon_sha256":"d1221068e9f359294e8595188a5177b7d81837b9fcc523c91ad7ad3867a63888"},"schema_version":"1.0","source":{"id":"2110.11205","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.11205","created_at":"2026-07-05T05:35:11Z"},{"alias_kind":"arxiv_version","alias_value":"2110.11205v3","created_at":"2026-07-05T05:35:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.11205","created_at":"2026-07-05T05:35:11Z"},{"alias_kind":"pith_short_12","alias_value":"I7MBTB6YJFVC","created_at":"2026-07-05T05:35:11Z"},{"alias_kind":"pith_short_16","alias_value":"I7MBTB6YJFVCSVAI","created_at":"2026-07-05T05:35:11Z"},{"alias_kind":"pith_short_8","alias_value":"I7MBTB6Y","created_at":"2026-07-05T05:35:11Z"}],"graph_snapshots":[{"event_id":"sha256:c60829b893529197cee3d427f0ba1cca455b857501344c4bd866bd31877881a7","target":"graph","created_at":"2026-07-05T05:35:11Z","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/2110.11205/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While deep learning through empirical risk minimization (ERM) has succeeded at achieving human-level performance at a variety of complex tasks, ERM is not robust to distribution shifts or adversarial attacks. Synthetic data augmentation followed by empirical risk minimization (DA-ERM) is a simple and widely used solution to improve robustness in ERM. In addition, consistency regularization can be applied to further improve the robustness of the model by forcing the representation of the original sample and the augmented one to be similar. However, existing consistency regularization methods ar","authors_text":"Ahmad Beirami, Alborz Geramifard, Chinnadhurai Sankar, Meisam Razaviyayn, Pooyan Amini, Satwik Kottur, Shaunak Halbe, Tianjian Huang","cross_cats":["cs.AI","cs.CL","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T15:30:40Z","title":"Robustness through Data Augmentation Loss Consistency"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.11205","kind":"arxiv","version":3},"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":"sha256:7983ae13dfb5f5ab298ae1aa1aca14dec69ebe1b3800bd47fbe6b6a7bd865706","target":"record","created_at":"2026-07-05T05:35:11Z","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":"9f229db14e4446a3f09f29dbc26b0af0ebf09535feeac55b6b3c3a4d98bfece1","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T15:30:40Z","title_canon_sha256":"d1221068e9f359294e8595188a5177b7d81837b9fcc523c91ad7ad3867a63888"},"schema_version":"1.0","source":{"id":"2110.11205","kind":"arxiv","version":3}},"canonical_sha256":"47d81987d8496a29540892bda914f83010d04faf768fd6d12b79882c5560141b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"47d81987d8496a29540892bda914f83010d04faf768fd6d12b79882c5560141b","first_computed_at":"2026-07-05T05:35:11.828545Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:35:11.828545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S2XMbsIbBKCszuxwXLFPRICDsGY095Z2cR2XmvAjw5WGJnf7gMn4gPFO6Elj1yHxR2zYYs5GgXF5yFxnFr5CBA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:35:11.829180Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.11205","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7983ae13dfb5f5ab298ae1aa1aca14dec69ebe1b3800bd47fbe6b6a7bd865706","sha256:c60829b893529197cee3d427f0ba1cca455b857501344c4bd866bd31877881a7"],"state_sha256":"cd4869daefc1bc37482c7c5a8698a990cec534d072dafc4273e3bb4c185dd034"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AVxDHtsP+qNvmaK4in8R3QkEWDnvoMwsxL6rRK1FOGJMrCE/2nVwoAnB/NC1Y2hbBzjdJiT1qR6yv1ZXwZNWAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T03:03:24.886050Z","bundle_sha256":"5c481d75c6347e1745240622b0c9cc0a574ea7bd44495a145f314be8db3f17d9"}}