{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:62QY4MC7SQESV6FKOIVACNNDAR","short_pith_number":"pith:62QY4MC7","canonical_record":{"source":{"id":"1802.08250","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-22T10:23:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c751df35925ee7a239ea04c4a16052aba1c8b31e5b679930f25485f6a743047c","abstract_canon_sha256":"39746cae6eb76bdd945d29bcecb43b5fbc8d1cee36ce16559a133d9229619f06"},"schema_version":"1.0"},"canonical_sha256":"f6a18e305f94092af8aa722a0135a30445224b00ea71986ea7562d23cf72c2a1","source":{"kind":"arxiv","id":"1802.08250","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.08250","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"arxiv_version","alias_value":"1802.08250v2","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08250","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"pith_short_12","alias_value":"62QY4MC7SQES","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"62QY4MC7SQESV6FK","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"62QY4MC7","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:62QY4MC7SQESV6FKOIVACNNDAR","target":"record","payload":{"canonical_record":{"source":{"id":"1802.08250","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-22T10:23:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c751df35925ee7a239ea04c4a16052aba1c8b31e5b679930f25485f6a743047c","abstract_canon_sha256":"39746cae6eb76bdd945d29bcecb43b5fbc8d1cee36ce16559a133d9229619f06"},"schema_version":"1.0"},"canonical_sha256":"f6a18e305f94092af8aa722a0135a30445224b00ea71986ea7562d23cf72c2a1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:21.166927Z","signature_b64":"w43XGpBCDQNDTZXLbqQRTRnk/NWWb7aq3p8Gzos1OU6h0lGj4A70lqE+Vh6CExO6SzWDCMPisFkM+fd8Y4yBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6a18e305f94092af8aa722a0135a30445224b00ea71986ea7562d23cf72c2a1","last_reissued_at":"2026-05-18T00:16:21.166476Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:21.166476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.08250","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":"2026-05-18T00:16:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AeeLjwtv0t9iIftJZfFlUnNbGyYV+3db6SOUhy9u0fMnWJBoOcfWJs2JNHI8t+fMJG5Fda1UUerw3ZUDjrbnCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:38:44.806526Z"},"content_sha256":"490c9daf17ecd08b1d5a10b669e9a01195557ef892b03b5a7e153b2865c83571","schema_version":"1.0","event_id":"sha256:490c9daf17ecd08b1d5a10b669e9a01195557ef892b03b5a7e153b2865c83571"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:62QY4MC7SQESV6FKOIVACNNDAR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Abel S. Zacarias, Lu\\'is A. Alexandre","submitted_at":"2018-02-22T10:23:36Z","abstract_excerpt":"Lifelong learning aims to develop machine learning systems that can learn new tasks while preserving the performance on previous learned tasks. In this paper we present a method to overcome catastrophic forgetting on convolutional neural networks, that learns new tasks and preserves the performance on old tasks without accessing the data of the original model, by selective network augmentation. The experiment results showed that SeNA-CNN, in some scenarios, outperforms the state-of-art Learning without Forgetting algorithm. Results also showed that in some situations it is better to use SeNA-C"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08250","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":""},"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":"2026-05-18T00:16:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wvgS55NsMv7sdzsdtU5+24KugwAtgM+a0g56/k0tgpit6kzp5Og/3pfo/7/cvKp/Uy6JdCKgQ5I8LTAG1WOxDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:38:44.807161Z"},"content_sha256":"9751d1009397ab950cddaeae856f35700aeb634672ce7a1def546e146244b748","schema_version":"1.0","event_id":"sha256:9751d1009397ab950cddaeae856f35700aeb634672ce7a1def546e146244b748"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/62QY4MC7SQESV6FKOIVACNNDAR/bundle.json","state_url":"https://pith.science/pith/62QY4MC7SQESV6FKOIVACNNDAR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/62QY4MC7SQESV6FKOIVACNNDAR/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":"2026-05-27T11:38:44Z","links":{"resolver":"https://pith.science/pith/62QY4MC7SQESV6FKOIVACNNDAR","bundle":"https://pith.science/pith/62QY4MC7SQESV6FKOIVACNNDAR/bundle.json","state":"https://pith.science/pith/62QY4MC7SQESV6FKOIVACNNDAR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/62QY4MC7SQESV6FKOIVACNNDAR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:62QY4MC7SQESV6FKOIVACNNDAR","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":"39746cae6eb76bdd945d29bcecb43b5fbc8d1cee36ce16559a133d9229619f06","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-22T10:23:36Z","title_canon_sha256":"c751df35925ee7a239ea04c4a16052aba1c8b31e5b679930f25485f6a743047c"},"schema_version":"1.0","source":{"id":"1802.08250","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.08250","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"arxiv_version","alias_value":"1802.08250v2","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08250","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"pith_short_12","alias_value":"62QY4MC7SQES","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"62QY4MC7SQESV6FK","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"62QY4MC7","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:9751d1009397ab950cddaeae856f35700aeb634672ce7a1def546e146244b748","target":"graph","created_at":"2026-05-18T00:16:21Z","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":"Lifelong learning aims to develop machine learning systems that can learn new tasks while preserving the performance on previous learned tasks. In this paper we present a method to overcome catastrophic forgetting on convolutional neural networks, that learns new tasks and preserves the performance on old tasks without accessing the data of the original model, by selective network augmentation. The experiment results showed that SeNA-CNN, in some scenarios, outperforms the state-of-art Learning without Forgetting algorithm. Results also showed that in some situations it is better to use SeNA-C","authors_text":"Abel S. Zacarias, Lu\\'is A. Alexandre","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-22T10:23:36Z","title":"SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08250","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":"sha256:490c9daf17ecd08b1d5a10b669e9a01195557ef892b03b5a7e153b2865c83571","target":"record","created_at":"2026-05-18T00:16:21Z","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":"39746cae6eb76bdd945d29bcecb43b5fbc8d1cee36ce16559a133d9229619f06","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-22T10:23:36Z","title_canon_sha256":"c751df35925ee7a239ea04c4a16052aba1c8b31e5b679930f25485f6a743047c"},"schema_version":"1.0","source":{"id":"1802.08250","kind":"arxiv","version":2}},"canonical_sha256":"f6a18e305f94092af8aa722a0135a30445224b00ea71986ea7562d23cf72c2a1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f6a18e305f94092af8aa722a0135a30445224b00ea71986ea7562d23cf72c2a1","first_computed_at":"2026-05-18T00:16:21.166476Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:21.166476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w43XGpBCDQNDTZXLbqQRTRnk/NWWb7aq3p8Gzos1OU6h0lGj4A70lqE+Vh6CExO6SzWDCMPisFkM+fd8Y4yBBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:21.166927Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.08250","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:490c9daf17ecd08b1d5a10b669e9a01195557ef892b03b5a7e153b2865c83571","sha256:9751d1009397ab950cddaeae856f35700aeb634672ce7a1def546e146244b748"],"state_sha256":"f71c8b52bc95bc79c2c851b90a9f0d3e21493e1046718ae48e4cd89ce34e59d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"euXO/D+swkU/Ax851UWhHuP33GswmGWilY48CfDHCaRn/zFZvEYi9wRJKF6CfqBh4FZkGn4ku6xfVHy/epnJCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T11:38:44.810426Z","bundle_sha256":"8509c879fe3ace251aac61ee03f9af2c859cb6f49bf3c86f4b570bd175d66aeb"}}