{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:HSIBOZEBBNUR6OIFNLAFCDYUNG","short_pith_number":"pith:HSIBOZEB","canonical_record":{"source":{"id":"1911.09665","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-21T18:53:23Z","cross_cats_sorted":[],"title_canon_sha256":"53b79994048fc0c99223c904f98a20ef61cceffddea7b8e2a78bf3378b308043","abstract_canon_sha256":"37ba4aab8f437050a25d03e9d107dfbe97c5e120426ad2b66553aeae8f0bf768"},"schema_version":"1.0"},"canonical_sha256":"3c901764810b691f39056ac0510f14698f144db2f90633f2edd13c90225a00db","source":{"kind":"arxiv","id":"1911.09665","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.09665","created_at":"2026-07-05T00:55:06Z"},{"alias_kind":"arxiv_version","alias_value":"1911.09665v2","created_at":"2026-07-05T00:55:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.09665","created_at":"2026-07-05T00:55:06Z"},{"alias_kind":"pith_short_12","alias_value":"HSIBOZEBBNUR","created_at":"2026-07-05T00:55:06Z"},{"alias_kind":"pith_short_16","alias_value":"HSIBOZEBBNUR6OIF","created_at":"2026-07-05T00:55:06Z"},{"alias_kind":"pith_short_8","alias_value":"HSIBOZEB","created_at":"2026-07-05T00:55:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:HSIBOZEBBNUR6OIFNLAFCDYUNG","target":"record","payload":{"canonical_record":{"source":{"id":"1911.09665","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-21T18:53:23Z","cross_cats_sorted":[],"title_canon_sha256":"53b79994048fc0c99223c904f98a20ef61cceffddea7b8e2a78bf3378b308043","abstract_canon_sha256":"37ba4aab8f437050a25d03e9d107dfbe97c5e120426ad2b66553aeae8f0bf768"},"schema_version":"1.0"},"canonical_sha256":"3c901764810b691f39056ac0510f14698f144db2f90633f2edd13c90225a00db","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:55:06.604142Z","signature_b64":"9LIQpieVd0cpaoP8tJu9+3z2fCBOHjfnO/nDvQAJDIKDcuCHSBRQ4IRslYls98JyrmALkjMh6MK4mpK+ifn8DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c901764810b691f39056ac0510f14698f144db2f90633f2edd13c90225a00db","last_reissued_at":"2026-07-05T00:55:06.603718Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:55:06.603718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1911.09665","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":"2026-07-05T00:55:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xlRFPhtyiXYGfeNDEDT/mO2GL7x/0WxoPT9yLwZa4nqQJMI8zyhcApHoFwy9K/PRGNV67snRNiCwhFOs58IfCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T23:08:37.552626Z"},"content_sha256":"c34c2836137e931d38acca8c138d6efbba31b910a64c802e220a0dd19da818c3","schema_version":"1.0","event_id":"sha256:c34c2836137e931d38acca8c138d6efbba31b910a64c802e220a0dd19da818c3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:HSIBOZEBBNUR6OIFNLAFCDYUNG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adversarial Examples Improve Image Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan Yuille, Boqing Gong, Cihang Xie, Jiang Wang, Mingxing Tan, Quoc V. Le","submitted_at":"2019-11-21T18:53:23Z","abstract_excerpt":"Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if harnessed in the right manner. We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to our method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.\n  We show that AdvProp improves a wide range of models on various image recognition task"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.09665","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/1911.09665/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":"2026-07-05T00:55:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JdOKzKoLji6Fl3zXrpKj1/PnXsGS4876C+5MFz0DO/E+PZUsah/Vn04G7JNVqCry4jFI38aaCYx8pzfVdr20Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T23:08:37.553171Z"},"content_sha256":"9eb62209c94f33ba2c8d2343017e10bb20872eb6f0ab84cd820b2a74e7665fb6","schema_version":"1.0","event_id":"sha256:9eb62209c94f33ba2c8d2343017e10bb20872eb6f0ab84cd820b2a74e7665fb6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HSIBOZEBBNUR6OIFNLAFCDYUNG/bundle.json","state_url":"https://pith.science/pith/HSIBOZEBBNUR6OIFNLAFCDYUNG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HSIBOZEBBNUR6OIFNLAFCDYUNG/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":"2026-08-04T23:08:37Z","links":{"resolver":"https://pith.science/pith/HSIBOZEBBNUR6OIFNLAFCDYUNG","bundle":"https://pith.science/pith/HSIBOZEBBNUR6OIFNLAFCDYUNG/bundle.json","state":"https://pith.science/pith/HSIBOZEBBNUR6OIFNLAFCDYUNG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HSIBOZEBBNUR6OIFNLAFCDYUNG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:HSIBOZEBBNUR6OIFNLAFCDYUNG","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":"37ba4aab8f437050a25d03e9d107dfbe97c5e120426ad2b66553aeae8f0bf768","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-21T18:53:23Z","title_canon_sha256":"53b79994048fc0c99223c904f98a20ef61cceffddea7b8e2a78bf3378b308043"},"schema_version":"1.0","source":{"id":"1911.09665","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.09665","created_at":"2026-07-05T00:55:06Z"},{"alias_kind":"arxiv_version","alias_value":"1911.09665v2","created_at":"2026-07-05T00:55:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.09665","created_at":"2026-07-05T00:55:06Z"},{"alias_kind":"pith_short_12","alias_value":"HSIBOZEBBNUR","created_at":"2026-07-05T00:55:06Z"},{"alias_kind":"pith_short_16","alias_value":"HSIBOZEBBNUR6OIF","created_at":"2026-07-05T00:55:06Z"},{"alias_kind":"pith_short_8","alias_value":"HSIBOZEB","created_at":"2026-07-05T00:55:06Z"}],"graph_snapshots":[{"event_id":"sha256:9eb62209c94f33ba2c8d2343017e10bb20872eb6f0ab84cd820b2a74e7665fb6","target":"graph","created_at":"2026-07-05T00:55:06Z","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/1911.09665/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if harnessed in the right manner. We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to our method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.\n  We show that AdvProp improves a wide range of models on various image recognition task","authors_text":"Alan Yuille, Boqing Gong, Cihang Xie, Jiang Wang, Mingxing Tan, Quoc V. Le","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-21T18:53:23Z","title":"Adversarial Examples Improve Image Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.09665","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:c34c2836137e931d38acca8c138d6efbba31b910a64c802e220a0dd19da818c3","target":"record","created_at":"2026-07-05T00:55:06Z","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":"37ba4aab8f437050a25d03e9d107dfbe97c5e120426ad2b66553aeae8f0bf768","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-21T18:53:23Z","title_canon_sha256":"53b79994048fc0c99223c904f98a20ef61cceffddea7b8e2a78bf3378b308043"},"schema_version":"1.0","source":{"id":"1911.09665","kind":"arxiv","version":2}},"canonical_sha256":"3c901764810b691f39056ac0510f14698f144db2f90633f2edd13c90225a00db","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3c901764810b691f39056ac0510f14698f144db2f90633f2edd13c90225a00db","first_computed_at":"2026-07-05T00:55:06.603718Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:55:06.603718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9LIQpieVd0cpaoP8tJu9+3z2fCBOHjfnO/nDvQAJDIKDcuCHSBRQ4IRslYls98JyrmALkjMh6MK4mpK+ifn8DA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:55:06.604142Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.09665","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c34c2836137e931d38acca8c138d6efbba31b910a64c802e220a0dd19da818c3","sha256:9eb62209c94f33ba2c8d2343017e10bb20872eb6f0ab84cd820b2a74e7665fb6"],"state_sha256":"1fbb4b987fdeb5ee2f4ce64562b1908d8576fdc90f86da2421ebeb67c29616e6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N2EWMhjEEt6+4pFShsSGLG5jVzQi1dQVO7NYUEuqnrOqtX+rKBBZNbOZunLnQ8UeGwoEsoX9ON+EXmzoT3rkAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T23:08:37.558374Z","bundle_sha256":"96ce41d8ce09cff1306f4615f5b98d7430324606e5eac7e18d3fc4ead88938ca"}}