{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:BP2Q65W5QLTEGHBKAL56FEYLQU","short_pith_number":"pith:BP2Q65W5","canonical_record":{"source":{"id":"2311.17607","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-29T13:05:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c41158ef4f155be0bba456a2e1fd180106d50ddb460e831c089afbc1d4744e61","abstract_canon_sha256":"497c6a3e38a9cdc4426fe7c1495e889d4b1af0c617a23b661e7c383aa2a9a4f0"},"schema_version":"1.0"},"canonical_sha256":"0bf50f76dd82e6431c2a02fbe2930b8522699d02df62edfdec09c6be80a6fb38","source":{"kind":"arxiv","id":"2311.17607","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.17607","created_at":"2026-07-05T08:56:15Z"},{"alias_kind":"arxiv_version","alias_value":"2311.17607v2","created_at":"2026-07-05T08:56:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.17607","created_at":"2026-07-05T08:56:15Z"},{"alias_kind":"pith_short_12","alias_value":"BP2Q65W5QLTE","created_at":"2026-07-05T08:56:15Z"},{"alias_kind":"pith_short_16","alias_value":"BP2Q65W5QLTEGHBK","created_at":"2026-07-05T08:56:15Z"},{"alias_kind":"pith_short_8","alias_value":"BP2Q65W5","created_at":"2026-07-05T08:56:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:BP2Q65W5QLTEGHBKAL56FEYLQU","target":"record","payload":{"canonical_record":{"source":{"id":"2311.17607","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-29T13:05:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c41158ef4f155be0bba456a2e1fd180106d50ddb460e831c089afbc1d4744e61","abstract_canon_sha256":"497c6a3e38a9cdc4426fe7c1495e889d4b1af0c617a23b661e7c383aa2a9a4f0"},"schema_version":"1.0"},"canonical_sha256":"0bf50f76dd82e6431c2a02fbe2930b8522699d02df62edfdec09c6be80a6fb38","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:56:15.361137Z","signature_b64":"Ix6crpB0NS52B1DJR1BSfQ3l4ss3dwzwYcZLGJZCY3s3wjUy1lXtH/BbpUQoILxxDtKYxaSDuv7zmIpDbip1DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0bf50f76dd82e6431c2a02fbe2930b8522699d02df62edfdec09c6be80a6fb38","last_reissued_at":"2026-07-05T08:56:15.360717Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:56:15.360717Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.17607","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":"2026-07-05T08:56:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JWlm9otVrK/hXQQ442S6BqzUqHYKVNocyEPgKAaU1ZNXXjuZkyYPjysTSjgmIcZEMsx+2gw54Nn2WmBPfkLjDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T17:23:28.815132Z"},"content_sha256":"6c812fdfc8168512aefe456f701ae1567eabc0fe56713919b6d5839b86d66aab","schema_version":"1.0","event_id":"sha256:6c812fdfc8168512aefe456f701ae1567eabc0fe56713919b6d5839b86d66aab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:BP2Q65W5QLTEGHBKAL56FEYLQU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Topology-preserving Adversarial Training for Alleviating Natural Accuracy Degradation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Danding Wang, Fan Tang, Juan Cao, Peng Li, Sheng Tang, Xiaoyue Mi, Yang Liu, Yepeng Weng","submitted_at":"2023-11-29T13:05:06Z","abstract_excerpt":"Despite the effectiveness in improving the robustness of neural networks, adversarial training has suffered from the natural accuracy degradation problem, i.e., accuracy on natural samples has reduced significantly. In this study, we reveal that natural accuracy degradation is highly related to the disruption of the natural sample topology in the representation space by quantitative and qualitative experiments. Based on this observation, we propose Topology-pReserving Adversarial traINing (TRAIN) to alleviate the problem by preserving the topology structure of natural samples from a standard m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.17607","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/2311.17607/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":"2026-07-05T08:56:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rmEJdqL7RChZ9amWmcZ9ZUTmR+gtGQ7BbLesQXq/nOltVCyQInRM7/Nmo23cDfmzjYoxKxdWabNAlLy9rjk0AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T17:23:28.815516Z"},"content_sha256":"c5db9d5d19982d4d80089d329d5ac781aa4dd6639440adb97107f81e8419defa","schema_version":"1.0","event_id":"sha256:c5db9d5d19982d4d80089d329d5ac781aa4dd6639440adb97107f81e8419defa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BP2Q65W5QLTEGHBKAL56FEYLQU/bundle.json","state_url":"https://pith.science/pith/BP2Q65W5QLTEGHBKAL56FEYLQU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BP2Q65W5QLTEGHBKAL56FEYLQU/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":"2026-07-13T17:23:28Z","links":{"resolver":"https://pith.science/pith/BP2Q65W5QLTEGHBKAL56FEYLQU","bundle":"https://pith.science/pith/BP2Q65W5QLTEGHBKAL56FEYLQU/bundle.json","state":"https://pith.science/pith/BP2Q65W5QLTEGHBKAL56FEYLQU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BP2Q65W5QLTEGHBKAL56FEYLQU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:BP2Q65W5QLTEGHBKAL56FEYLQU","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":"497c6a3e38a9cdc4426fe7c1495e889d4b1af0c617a23b661e7c383aa2a9a4f0","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-29T13:05:06Z","title_canon_sha256":"c41158ef4f155be0bba456a2e1fd180106d50ddb460e831c089afbc1d4744e61"},"schema_version":"1.0","source":{"id":"2311.17607","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.17607","created_at":"2026-07-05T08:56:15Z"},{"alias_kind":"arxiv_version","alias_value":"2311.17607v2","created_at":"2026-07-05T08:56:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.17607","created_at":"2026-07-05T08:56:15Z"},{"alias_kind":"pith_short_12","alias_value":"BP2Q65W5QLTE","created_at":"2026-07-05T08:56:15Z"},{"alias_kind":"pith_short_16","alias_value":"BP2Q65W5QLTEGHBK","created_at":"2026-07-05T08:56:15Z"},{"alias_kind":"pith_short_8","alias_value":"BP2Q65W5","created_at":"2026-07-05T08:56:15Z"}],"graph_snapshots":[{"event_id":"sha256:c5db9d5d19982d4d80089d329d5ac781aa4dd6639440adb97107f81e8419defa","target":"graph","created_at":"2026-07-05T08:56:15Z","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/2311.17607/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite the effectiveness in improving the robustness of neural networks, adversarial training has suffered from the natural accuracy degradation problem, i.e., accuracy on natural samples has reduced significantly. In this study, we reveal that natural accuracy degradation is highly related to the disruption of the natural sample topology in the representation space by quantitative and qualitative experiments. Based on this observation, we propose Topology-pReserving Adversarial traINing (TRAIN) to alleviate the problem by preserving the topology structure of natural samples from a standard m","authors_text":"Danding Wang, Fan Tang, Juan Cao, Peng Li, Sheng Tang, Xiaoyue Mi, Yang Liu, Yepeng Weng","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-29T13:05:06Z","title":"Topology-preserving Adversarial Training for Alleviating Natural Accuracy Degradation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.17607","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":"sha256:6c812fdfc8168512aefe456f701ae1567eabc0fe56713919b6d5839b86d66aab","target":"record","created_at":"2026-07-05T08:56:15Z","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":"497c6a3e38a9cdc4426fe7c1495e889d4b1af0c617a23b661e7c383aa2a9a4f0","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-29T13:05:06Z","title_canon_sha256":"c41158ef4f155be0bba456a2e1fd180106d50ddb460e831c089afbc1d4744e61"},"schema_version":"1.0","source":{"id":"2311.17607","kind":"arxiv","version":2}},"canonical_sha256":"0bf50f76dd82e6431c2a02fbe2930b8522699d02df62edfdec09c6be80a6fb38","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0bf50f76dd82e6431c2a02fbe2930b8522699d02df62edfdec09c6be80a6fb38","first_computed_at":"2026-07-05T08:56:15.360717Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:56:15.360717Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ix6crpB0NS52B1DJR1BSfQ3l4ss3dwzwYcZLGJZCY3s3wjUy1lXtH/BbpUQoILxxDtKYxaSDuv7zmIpDbip1DA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:56:15.361137Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.17607","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6c812fdfc8168512aefe456f701ae1567eabc0fe56713919b6d5839b86d66aab","sha256:c5db9d5d19982d4d80089d329d5ac781aa4dd6639440adb97107f81e8419defa"],"state_sha256":"02b4e7b59ec46022a1b6b1dd4924868933fb2033c2c6676895eb49122102b311"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EqSNI3QvKIzlNw/VoSUEOn0B+HJzahRrUCdHD3TnroNOf525CKRZNP+kPzoI5ERXvapShi8osTXIdlmKYVBoCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-13T17:23:28.817838Z","bundle_sha256":"3836920f3ea0dafff1199137051b500537b48f03d019a6b1d8e665ad397b9d38"}}