{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IKI3K4VSXSXZZJSTBTJRC4GZHT","short_pith_number":"pith:IKI3K4VS","canonical_record":{"source":{"id":"1811.07457","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-19T02:06:09Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3f8cdc95252a88cb1f53fdc19600f2e6c4e089edee318ebafbebf8e308ab915c","abstract_canon_sha256":"4d964c74d1f8a67d313c70c4467df38917d83c88f4e24e48a8907af34b9231a8"},"schema_version":"1.0"},"canonical_sha256":"4291b572b2bcaf9ca6530cd31170d93ce8cc40dc33b483613b93a8696098110d","source":{"kind":"arxiv","id":"1811.07457","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.07457","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"arxiv_version","alias_value":"1811.07457v1","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.07457","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"pith_short_12","alias_value":"IKI3K4VSXSXZ","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IKI3K4VSXSXZZJST","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IKI3K4VS","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IKI3K4VSXSXZZJSTBTJRC4GZHT","target":"record","payload":{"canonical_record":{"source":{"id":"1811.07457","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-19T02:06:09Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3f8cdc95252a88cb1f53fdc19600f2e6c4e089edee318ebafbebf8e308ab915c","abstract_canon_sha256":"4d964c74d1f8a67d313c70c4467df38917d83c88f4e24e48a8907af34b9231a8"},"schema_version":"1.0"},"canonical_sha256":"4291b572b2bcaf9ca6530cd31170d93ce8cc40dc33b483613b93a8696098110d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:24.462771Z","signature_b64":"HTlxUNqxHiHbVFhuoHd/5nSfP/9Nna9g+CoNGTPbiKwPpNT/kVTUg51x0klc+ph5QG90p+UF6HOy8N5m9fPKBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4291b572b2bcaf9ca6530cd31170d93ce8cc40dc33b483613b93a8696098110d","last_reissued_at":"2026-05-18T00:00:24.462117Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:24.462117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.07457","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":"2026-05-18T00:00:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qKXfQpl+aGVR2qVRwaLWk5vB12WaFksn0WWYiH8TYVf96k9rEnpgioMw87elCxYCo3vJjsqNkdfOxVbIWhO0Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:43:54.834914Z"},"content_sha256":"a62a1debac8ccb0eb1276d68b4846692d8eddd64ba45fc6cb6ed0919c9b3ded1","schema_version":"1.0","event_id":"sha256:a62a1debac8ccb0eb1276d68b4846692d8eddd64ba45fc6cb6ed0919c9b3ded1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IKI3K4VSXSXZZJSTBTJRC4GZHT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generalizable Adversarial Training via Spectral Normalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"David Tse, Farzan Farnia, Jesse M. Zhang","submitted_at":"2018-11-19T02:06:09Z","abstract_excerpt":"Deep neural networks (DNNs) have set benchmarks on a wide array of supervised learning tasks. Trained DNNs, however, often lack robustness to minor adversarial perturbations to the input, which undermines their true practicality. Recent works have increased the robustness of DNNs by fitting networks using adversarially-perturbed training samples, but the improved performance can still be far below the performance seen in non-adversarial settings. A significant portion of this gap can be attributed to the decrease in generalization performance due to adversarial training. In this work, we exten"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.07457","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":""},"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":"2026-05-18T00:00:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zAAgjIk8oWXEiMIOHwZF8tkPJfFp8lJNCaLqXYtJtJUXSbJUGQOoldjCYoO8RLxxMbo0YxGvbJiPkTXoTFQPDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:43:54.835291Z"},"content_sha256":"ff20fede8fb5636327dd502b92ab18e9efdc3a9fb663e47275ce3210c1e423b8","schema_version":"1.0","event_id":"sha256:ff20fede8fb5636327dd502b92ab18e9efdc3a9fb663e47275ce3210c1e423b8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IKI3K4VSXSXZZJSTBTJRC4GZHT/bundle.json","state_url":"https://pith.science/pith/IKI3K4VSXSXZZJSTBTJRC4GZHT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IKI3K4VSXSXZZJSTBTJRC4GZHT/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":"2026-05-26T03:43:54Z","links":{"resolver":"https://pith.science/pith/IKI3K4VSXSXZZJSTBTJRC4GZHT","bundle":"https://pith.science/pith/IKI3K4VSXSXZZJSTBTJRC4GZHT/bundle.json","state":"https://pith.science/pith/IKI3K4VSXSXZZJSTBTJRC4GZHT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IKI3K4VSXSXZZJSTBTJRC4GZHT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IKI3K4VSXSXZZJSTBTJRC4GZHT","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":"4d964c74d1f8a67d313c70c4467df38917d83c88f4e24e48a8907af34b9231a8","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-19T02:06:09Z","title_canon_sha256":"3f8cdc95252a88cb1f53fdc19600f2e6c4e089edee318ebafbebf8e308ab915c"},"schema_version":"1.0","source":{"id":"1811.07457","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.07457","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"arxiv_version","alias_value":"1811.07457v1","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.07457","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"pith_short_12","alias_value":"IKI3K4VSXSXZ","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IKI3K4VSXSXZZJST","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IKI3K4VS","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:ff20fede8fb5636327dd502b92ab18e9efdc3a9fb663e47275ce3210c1e423b8","target":"graph","created_at":"2026-05-18T00:00:24Z","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"},"paper":{"abstract_excerpt":"Deep neural networks (DNNs) have set benchmarks on a wide array of supervised learning tasks. Trained DNNs, however, often lack robustness to minor adversarial perturbations to the input, which undermines their true practicality. Recent works have increased the robustness of DNNs by fitting networks using adversarially-perturbed training samples, but the improved performance can still be far below the performance seen in non-adversarial settings. A significant portion of this gap can be attributed to the decrease in generalization performance due to adversarial training. In this work, we exten","authors_text":"David Tse, Farzan Farnia, Jesse M. Zhang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-19T02:06:09Z","title":"Generalizable Adversarial Training via Spectral Normalization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.07457","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:a62a1debac8ccb0eb1276d68b4846692d8eddd64ba45fc6cb6ed0919c9b3ded1","target":"record","created_at":"2026-05-18T00:00:24Z","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":"4d964c74d1f8a67d313c70c4467df38917d83c88f4e24e48a8907af34b9231a8","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-19T02:06:09Z","title_canon_sha256":"3f8cdc95252a88cb1f53fdc19600f2e6c4e089edee318ebafbebf8e308ab915c"},"schema_version":"1.0","source":{"id":"1811.07457","kind":"arxiv","version":1}},"canonical_sha256":"4291b572b2bcaf9ca6530cd31170d93ce8cc40dc33b483613b93a8696098110d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4291b572b2bcaf9ca6530cd31170d93ce8cc40dc33b483613b93a8696098110d","first_computed_at":"2026-05-18T00:00:24.462117Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:24.462117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HTlxUNqxHiHbVFhuoHd/5nSfP/9Nna9g+CoNGTPbiKwPpNT/kVTUg51x0klc+ph5QG90p+UF6HOy8N5m9fPKBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:24.462771Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.07457","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a62a1debac8ccb0eb1276d68b4846692d8eddd64ba45fc6cb6ed0919c9b3ded1","sha256:ff20fede8fb5636327dd502b92ab18e9efdc3a9fb663e47275ce3210c1e423b8"],"state_sha256":"272fa5563fcbf5b5fab4710d9f4eae5fc38389413c5ba2555ba491569755279d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ujQEQUKBnDXEtZOVPfaYNpc2blPlqc109dmmsBoZVfJH0iOR7P8Sv1NYmDgkugzwuElVbYml+BgWbwtc68uGDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T03:43:54.837564Z","bundle_sha256":"2290a6036dd1c4eb895c5272a3515f54bfa87c8ce029cf776fcaeb585eab07da"}}