{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:Z4A67UB6N4UQZY6V4RCSCREYC7","short_pith_number":"pith:Z4A67UB6","canonical_record":{"source":{"id":"2210.14783","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T15:21:39Z","cross_cats_sorted":[],"title_canon_sha256":"6111336b746d0a52147560c989910c7ff5ed00fcf04dc9436924ee33a9f16409","abstract_canon_sha256":"9fd5939823caa9fed5dae9b24c2032db207afe1700f9fe1fb669319dd309c677"},"schema_version":"1.0"},"canonical_sha256":"cf01efd03e6f290ce3d5e44521449817cdad0383b4eda11002cfa220b6c8355e","source":{"kind":"arxiv","id":"2210.14783","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.14783","created_at":"2026-07-05T05:10:49Z"},{"alias_kind":"arxiv_version","alias_value":"2210.14783v1","created_at":"2026-07-05T05:10:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.14783","created_at":"2026-07-05T05:10:49Z"},{"alias_kind":"pith_short_12","alias_value":"Z4A67UB6N4UQ","created_at":"2026-07-05T05:10:49Z"},{"alias_kind":"pith_short_16","alias_value":"Z4A67UB6N4UQZY6V","created_at":"2026-07-05T05:10:49Z"},{"alias_kind":"pith_short_8","alias_value":"Z4A67UB6","created_at":"2026-07-05T05:10:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:Z4A67UB6N4UQZY6V4RCSCREYC7","target":"record","payload":{"canonical_record":{"source":{"id":"2210.14783","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T15:21:39Z","cross_cats_sorted":[],"title_canon_sha256":"6111336b746d0a52147560c989910c7ff5ed00fcf04dc9436924ee33a9f16409","abstract_canon_sha256":"9fd5939823caa9fed5dae9b24c2032db207afe1700f9fe1fb669319dd309c677"},"schema_version":"1.0"},"canonical_sha256":"cf01efd03e6f290ce3d5e44521449817cdad0383b4eda11002cfa220b6c8355e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:10:49.702877Z","signature_b64":"EEZFZaEvXeHetwpoVvrblbsZT3ER9NlRMitEyB6QhRDXDKZz0ErPgSBfyvMWu6wHEvlK5OvOLQJYfR44m7hZAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf01efd03e6f290ce3d5e44521449817cdad0383b4eda11002cfa220b6c8355e","last_reissued_at":"2026-07-05T05:10:49.702488Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:10:49.702488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.14783","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:10:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+BQWsAx3OuzuVxV9GpaUfbdbeHo9YBY6L97Cs25GsoQ3/lQ2vH+DvoY9eewzHJvuHmL1Eul+SIGgwXjWbThKDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:17:56.916412Z"},"content_sha256":"68d34d3d69fa0051d60c99fcb09777057df05e548e7138943b037615ae774b28","schema_version":"1.0","event_id":"sha256:68d34d3d69fa0051d60c99fcb09777057df05e548e7138943b037615ae774b28"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:Z4A67UB6N4UQZY6V4RCSCREYC7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Decoupled Mixup for Generalized Visual Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bernard Ghanem, Bing Li, Haoqian Wu, Haozhe Liu, Jinheng Xie, Wentian Zhang, Yawen Huang, Yefeng Zheng, Yuexiang Li, Ziqi Zhang","submitted_at":"2022-10-26T15:21:39Z","abstract_excerpt":"Convolutional neural networks (CNN) have demonstrated remarkable performance when the training and testing data are from the same distribution. However, such trained CNN models often largely degrade on testing data which is unseen and Out-Of-the-Distribution (OOD). To address this issue, we propose a novel \"Decoupled-Mixup\" method to train CNN models for OOD visual recognition. Different from previous work combining pairs of images homogeneously, our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combines these regions of image pairs to train "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.14783","kind":"arxiv","version":1},"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/2210.14783/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":"2026-07-05T05:10:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YeJoq7jpkN59+CZDLCFTEwadNOL57pUT+z/pfgnFYkWJytx42xFsMrgBopdwpg0piMb3BwsQstFG2yN2mPOrCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:17:56.917064Z"},"content_sha256":"d363e41639f6fd88b016b2768040eeb86f561b33b2abcad00fb0179b9a31c269","schema_version":"1.0","event_id":"sha256:d363e41639f6fd88b016b2768040eeb86f561b33b2abcad00fb0179b9a31c269"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z4A67UB6N4UQZY6V4RCSCREYC7/bundle.json","state_url":"https://pith.science/pith/Z4A67UB6N4UQZY6V4RCSCREYC7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z4A67UB6N4UQZY6V4RCSCREYC7/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":"2026-08-03T18:17:56Z","links":{"resolver":"https://pith.science/pith/Z4A67UB6N4UQZY6V4RCSCREYC7","bundle":"https://pith.science/pith/Z4A67UB6N4UQZY6V4RCSCREYC7/bundle.json","state":"https://pith.science/pith/Z4A67UB6N4UQZY6V4RCSCREYC7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z4A67UB6N4UQZY6V4RCSCREYC7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:Z4A67UB6N4UQZY6V4RCSCREYC7","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":"9fd5939823caa9fed5dae9b24c2032db207afe1700f9fe1fb669319dd309c677","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T15:21:39Z","title_canon_sha256":"6111336b746d0a52147560c989910c7ff5ed00fcf04dc9436924ee33a9f16409"},"schema_version":"1.0","source":{"id":"2210.14783","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.14783","created_at":"2026-07-05T05:10:49Z"},{"alias_kind":"arxiv_version","alias_value":"2210.14783v1","created_at":"2026-07-05T05:10:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.14783","created_at":"2026-07-05T05:10:49Z"},{"alias_kind":"pith_short_12","alias_value":"Z4A67UB6N4UQ","created_at":"2026-07-05T05:10:49Z"},{"alias_kind":"pith_short_16","alias_value":"Z4A67UB6N4UQZY6V","created_at":"2026-07-05T05:10:49Z"},{"alias_kind":"pith_short_8","alias_value":"Z4A67UB6","created_at":"2026-07-05T05:10:49Z"}],"graph_snapshots":[{"event_id":"sha256:d363e41639f6fd88b016b2768040eeb86f561b33b2abcad00fb0179b9a31c269","target":"graph","created_at":"2026-07-05T05:10:49Z","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/2210.14783/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Convolutional neural networks (CNN) have demonstrated remarkable performance when the training and testing data are from the same distribution. However, such trained CNN models often largely degrade on testing data which is unseen and Out-Of-the-Distribution (OOD). To address this issue, we propose a novel \"Decoupled-Mixup\" method to train CNN models for OOD visual recognition. Different from previous work combining pairs of images homogeneously, our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combines these regions of image pairs to train ","authors_text":"Bernard Ghanem, Bing Li, Haoqian Wu, Haozhe Liu, Jinheng Xie, Wentian Zhang, Yawen Huang, Yefeng Zheng, Yuexiang Li, Ziqi Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T15:21:39Z","title":"Decoupled Mixup for Generalized Visual Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.14783","kind":"arxiv","version":1},"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":"sha256:68d34d3d69fa0051d60c99fcb09777057df05e548e7138943b037615ae774b28","target":"record","created_at":"2026-07-05T05:10:49Z","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":"9fd5939823caa9fed5dae9b24c2032db207afe1700f9fe1fb669319dd309c677","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T15:21:39Z","title_canon_sha256":"6111336b746d0a52147560c989910c7ff5ed00fcf04dc9436924ee33a9f16409"},"schema_version":"1.0","source":{"id":"2210.14783","kind":"arxiv","version":1}},"canonical_sha256":"cf01efd03e6f290ce3d5e44521449817cdad0383b4eda11002cfa220b6c8355e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cf01efd03e6f290ce3d5e44521449817cdad0383b4eda11002cfa220b6c8355e","first_computed_at":"2026-07-05T05:10:49.702488Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:10:49.702488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EEZFZaEvXeHetwpoVvrblbsZT3ER9NlRMitEyB6QhRDXDKZz0ErPgSBfyvMWu6wHEvlK5OvOLQJYfR44m7hZAw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:10:49.702877Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.14783","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68d34d3d69fa0051d60c99fcb09777057df05e548e7138943b037615ae774b28","sha256:d363e41639f6fd88b016b2768040eeb86f561b33b2abcad00fb0179b9a31c269"],"state_sha256":"dc38b93cade7681f632b11f86206abf449c6f7d8b959a7a02c3ee75d6239bc35"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VoeCMpF7l64oHGkB+ImZ8H1Zuqz1ajzK9N7/2YfvmtoHxBPC3n8sUw+71QBFCbIB1FddyeFD+D6O8Z/7mBeFBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T18:17:56.947311Z","bundle_sha256":"67aa164e187c684598ef3dc9eb1b22ddea8d0c9b55681de99738ff7a64b15f09"}}