{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BCT5G2MOTC6PRGA4WW76Q5IQI2","short_pith_number":"pith:BCT5G2MO","canonical_record":{"source":{"id":"1906.03685","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-09T18:23:57Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"c27f0f2a662d691a52eca1a8ffcd462efa609cd6a81f7ac898d111605d5df826","abstract_canon_sha256":"6515973ac4218870c0038a306e766c96d044a0a7d9498bf534eaa54d66f057f5"},"schema_version":"1.0"},"canonical_sha256":"08a7d3698e98bcf8981cb5bfe8751046b8834d7592b4f4712bfd6d200d5be0ec","source":{"kind":"arxiv","id":"1906.03685","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.03685","created_at":"2026-05-17T23:43:45Z"},{"alias_kind":"arxiv_version","alias_value":"1906.03685v1","created_at":"2026-05-17T23:43:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.03685","created_at":"2026-05-17T23:43:45Z"},{"alias_kind":"pith_short_12","alias_value":"BCT5G2MOTC6P","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"BCT5G2MOTC6PRGA4","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"BCT5G2MO","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BCT5G2MOTC6PRGA4WW76Q5IQI2","target":"record","payload":{"canonical_record":{"source":{"id":"1906.03685","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-09T18:23:57Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"c27f0f2a662d691a52eca1a8ffcd462efa609cd6a81f7ac898d111605d5df826","abstract_canon_sha256":"6515973ac4218870c0038a306e766c96d044a0a7d9498bf534eaa54d66f057f5"},"schema_version":"1.0"},"canonical_sha256":"08a7d3698e98bcf8981cb5bfe8751046b8834d7592b4f4712bfd6d200d5be0ec","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:45.359236Z","signature_b64":"CrtfPnsyEo69ayBcdtS/yWcOhFIC9b7MB+ZHf4t24VfGXI5Hk/gaX8rpCkKfQKSmS8uB0ocgPgmozsjfkjDGBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"08a7d3698e98bcf8981cb5bfe8751046b8834d7592b4f4712bfd6d200d5be0ec","last_reissued_at":"2026-05-17T23:43:45.358653Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:45.358653Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.03685","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":"2026-05-17T23:43:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TmGy/o96kZ+N1T3KRk4+cLXZSYqXWRrP8YL1ga16JGgan0MC/bD2LzSSzzZx0kr7bh8xP1U09+wEBZG90Oa6DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T05:53:08.604726Z"},"content_sha256":"bf3363457d0a60993ce4c8206e433fcfa30cd08e6cc2e1efddfebf35d71108f3","schema_version":"1.0","event_id":"sha256:bf3363457d0a60993ce4c8206e433fcfa30cd08e6cc2e1efddfebf35d71108f3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BCT5G2MOTC6PRGA4WW76Q5IQI2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Novelty Detection via Network Saliency in Visual-based Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Man-Ki Yoon, Valerie Chen, Zhong Shao","submitted_at":"2019-06-09T18:23:57Z","abstract_excerpt":"Machine-learning driven safety-critical autonomous systems, such as self-driving cars, must be able to detect situations where its trained model is not able to make a trustworthy prediction. Often viewed as a black-box, it is non-obvious to determine when a model will make a safe decision and when it will make an erroneous, perhaps life-threatening one. Prior work on novelty detection deal with highly structured data and do not translate well to dynamic, real-world situations. This paper proposes a multi-step framework for the detection of novel scenarios in vision-based autonomous systems by "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.03685","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":"2026-05-17T23:43:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tkeNF7NVuxZso6mhdjSDtfEubhz2Sjnx9ytY3KG2ffxxMY4gjRdgRaeCAIH3Vxo9Lw2AAWr5QEBZ6Dnqqfo5Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T05:53:08.605349Z"},"content_sha256":"1db6553313189cb7facbdb6610dfcc61a9c972c65e0b419fe025eeb2e0e23a54","schema_version":"1.0","event_id":"sha256:1db6553313189cb7facbdb6610dfcc61a9c972c65e0b419fe025eeb2e0e23a54"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BCT5G2MOTC6PRGA4WW76Q5IQI2/bundle.json","state_url":"https://pith.science/pith/BCT5G2MOTC6PRGA4WW76Q5IQI2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BCT5G2MOTC6PRGA4WW76Q5IQI2/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":"2026-06-07T05:53:08Z","links":{"resolver":"https://pith.science/pith/BCT5G2MOTC6PRGA4WW76Q5IQI2","bundle":"https://pith.science/pith/BCT5G2MOTC6PRGA4WW76Q5IQI2/bundle.json","state":"https://pith.science/pith/BCT5G2MOTC6PRGA4WW76Q5IQI2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BCT5G2MOTC6PRGA4WW76Q5IQI2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BCT5G2MOTC6PRGA4WW76Q5IQI2","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":"6515973ac4218870c0038a306e766c96d044a0a7d9498bf534eaa54d66f057f5","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-09T18:23:57Z","title_canon_sha256":"c27f0f2a662d691a52eca1a8ffcd462efa609cd6a81f7ac898d111605d5df826"},"schema_version":"1.0","source":{"id":"1906.03685","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.03685","created_at":"2026-05-17T23:43:45Z"},{"alias_kind":"arxiv_version","alias_value":"1906.03685v1","created_at":"2026-05-17T23:43:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.03685","created_at":"2026-05-17T23:43:45Z"},{"alias_kind":"pith_short_12","alias_value":"BCT5G2MOTC6P","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"BCT5G2MOTC6PRGA4","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"BCT5G2MO","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:1db6553313189cb7facbdb6610dfcc61a9c972c65e0b419fe025eeb2e0e23a54","target":"graph","created_at":"2026-05-17T23:43:45Z","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":"Machine-learning driven safety-critical autonomous systems, such as self-driving cars, must be able to detect situations where its trained model is not able to make a trustworthy prediction. Often viewed as a black-box, it is non-obvious to determine when a model will make a safe decision and when it will make an erroneous, perhaps life-threatening one. Prior work on novelty detection deal with highly structured data and do not translate well to dynamic, real-world situations. This paper proposes a multi-step framework for the detection of novel scenarios in vision-based autonomous systems by ","authors_text":"Man-Ki Yoon, Valerie Chen, Zhong Shao","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-09T18:23:57Z","title":"Novelty Detection via Network Saliency in Visual-based Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.03685","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:bf3363457d0a60993ce4c8206e433fcfa30cd08e6cc2e1efddfebf35d71108f3","target":"record","created_at":"2026-05-17T23:43:45Z","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":"6515973ac4218870c0038a306e766c96d044a0a7d9498bf534eaa54d66f057f5","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-09T18:23:57Z","title_canon_sha256":"c27f0f2a662d691a52eca1a8ffcd462efa609cd6a81f7ac898d111605d5df826"},"schema_version":"1.0","source":{"id":"1906.03685","kind":"arxiv","version":1}},"canonical_sha256":"08a7d3698e98bcf8981cb5bfe8751046b8834d7592b4f4712bfd6d200d5be0ec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"08a7d3698e98bcf8981cb5bfe8751046b8834d7592b4f4712bfd6d200d5be0ec","first_computed_at":"2026-05-17T23:43:45.358653Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:45.358653Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CrtfPnsyEo69ayBcdtS/yWcOhFIC9b7MB+ZHf4t24VfGXI5Hk/gaX8rpCkKfQKSmS8uB0ocgPgmozsjfkjDGBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:45.359236Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.03685","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bf3363457d0a60993ce4c8206e433fcfa30cd08e6cc2e1efddfebf35d71108f3","sha256:1db6553313189cb7facbdb6610dfcc61a9c972c65e0b419fe025eeb2e0e23a54"],"state_sha256":"f6df7c274a59a2cb93e3ffc8fee92c620fc9938df966633f5fe8a23feb1fedf9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o1R9RnbTKWGAW5pQYkElqRLVY1EaJVl9j6Wu2JeCgQlYUYetul5k5sRHpgUsouPgvN9R9fcykQ6vrnmOzASbCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T05:53:08.608973Z","bundle_sha256":"5ccb7549ce1dab610e707c711801af879b0339849bc7cd477ab69fe17338d0c8"}}