{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:ELYUDSBR2OG2JLZA7V2AL2HRK7","short_pith_number":"pith:ELYUDSBR","canonical_record":{"source":{"id":"2111.11366","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-22T17:23:34Z","cross_cats_sorted":[],"title_canon_sha256":"10298a4bdac3a47c76b5f9b82c52f8399112e1c34de9059417dbc14b9c46e368","abstract_canon_sha256":"257804c0575dd9112c2b6a7e9eb6b262a55282135845b838083dc598b0cfd5d1"},"schema_version":"1.0"},"canonical_sha256":"22f141c831d38da4af20fd7405e8f157e4b94f48aa143f88ca27b0c6bd845cf3","source":{"kind":"arxiv","id":"2111.11366","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.11366","created_at":"2026-07-05T03:34:02Z"},{"alias_kind":"arxiv_version","alias_value":"2111.11366v1","created_at":"2026-07-05T03:34:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.11366","created_at":"2026-07-05T03:34:02Z"},{"alias_kind":"pith_short_12","alias_value":"ELYUDSBR2OG2","created_at":"2026-07-05T03:34:02Z"},{"alias_kind":"pith_short_16","alias_value":"ELYUDSBR2OG2JLZA","created_at":"2026-07-05T03:34:02Z"},{"alias_kind":"pith_short_8","alias_value":"ELYUDSBR","created_at":"2026-07-05T03:34:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:ELYUDSBR2OG2JLZA7V2AL2HRK7","target":"record","payload":{"canonical_record":{"source":{"id":"2111.11366","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-22T17:23:34Z","cross_cats_sorted":[],"title_canon_sha256":"10298a4bdac3a47c76b5f9b82c52f8399112e1c34de9059417dbc14b9c46e368","abstract_canon_sha256":"257804c0575dd9112c2b6a7e9eb6b262a55282135845b838083dc598b0cfd5d1"},"schema_version":"1.0"},"canonical_sha256":"22f141c831d38da4af20fd7405e8f157e4b94f48aa143f88ca27b0c6bd845cf3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:34:02.855100Z","signature_b64":"64VxXRpupseOCINw6JXiwoPgJONTOawVEpGdoQ3+jtTkR+s9PxyjAiWIcbkd/ENEo4LPR16LrEqHNzBxM2/qBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22f141c831d38da4af20fd7405e8f157e4b94f48aa143f88ca27b0c6bd845cf3","last_reissued_at":"2026-07-05T03:34:02.854545Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:34:02.854545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.11366","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":"2026-07-05T03:34:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a4MkXTlB4VRHn+w6EC3LxQbVfP+b/0yT0jv4rg8ihKTB8V/izrvbHC4OjUoLAHh4IbgEpzfQzCs1vj0g2wr5CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:52:18.693561Z"},"content_sha256":"455b14b88038dfa121bece4a2236c0969d007c8a169138d3811ac27d4ec0f6b2","schema_version":"1.0","event_id":"sha256:455b14b88038dfa121bece4a2236c0969d007c8a169138d3811ac27d4ec0f6b2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:ELYUDSBR2OG2JLZA7V2AL2HRK7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haoming Zhan, Hichem Sahbi","submitted_at":"2021-11-22T17:23:34Z","abstract_excerpt":"Deep neural networks (DNNs) have recently achieved a great success in computer vision and several related fields. Despite such progress, current neural architectures still suffer from catastrophic interference (a.k.a. forgetting) which obstructs DNNs to learn continually. While several state-of-the-art methods have been proposed to mitigate forgetting, these existing solutions are either highly rigid (as regularization) or time/memory demanding (as replay). An intermediate class of methods, based on dynamic networks, has been proposed in the literature and provides a reasonable balance between"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.11366","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/2111.11366/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":"2026-07-05T03:34:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hect2PuArC0x6mzkMdOoozSV12634KrHBo5gvn8taU6vk+STzXln0+qzb55su90WlsWlOFtDTOuvtNft0GPBDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:52:18.694068Z"},"content_sha256":"445c2e169ad39d823b671b03d843fb0e292489d12209e4842166bf30d47f723c","schema_version":"1.0","event_id":"sha256:445c2e169ad39d823b671b03d843fb0e292489d12209e4842166bf30d47f723c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ELYUDSBR2OG2JLZA7V2AL2HRK7/bundle.json","state_url":"https://pith.science/pith/ELYUDSBR2OG2JLZA7V2AL2HRK7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ELYUDSBR2OG2JLZA7V2AL2HRK7/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":"2026-08-08T23:52:18Z","links":{"resolver":"https://pith.science/pith/ELYUDSBR2OG2JLZA7V2AL2HRK7","bundle":"https://pith.science/pith/ELYUDSBR2OG2JLZA7V2AL2HRK7/bundle.json","state":"https://pith.science/pith/ELYUDSBR2OG2JLZA7V2AL2HRK7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ELYUDSBR2OG2JLZA7V2AL2HRK7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:ELYUDSBR2OG2JLZA7V2AL2HRK7","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":"257804c0575dd9112c2b6a7e9eb6b262a55282135845b838083dc598b0cfd5d1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-22T17:23:34Z","title_canon_sha256":"10298a4bdac3a47c76b5f9b82c52f8399112e1c34de9059417dbc14b9c46e368"},"schema_version":"1.0","source":{"id":"2111.11366","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.11366","created_at":"2026-07-05T03:34:02Z"},{"alias_kind":"arxiv_version","alias_value":"2111.11366v1","created_at":"2026-07-05T03:34:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.11366","created_at":"2026-07-05T03:34:02Z"},{"alias_kind":"pith_short_12","alias_value":"ELYUDSBR2OG2","created_at":"2026-07-05T03:34:02Z"},{"alias_kind":"pith_short_16","alias_value":"ELYUDSBR2OG2JLZA","created_at":"2026-07-05T03:34:02Z"},{"alias_kind":"pith_short_8","alias_value":"ELYUDSBR","created_at":"2026-07-05T03:34:02Z"}],"graph_snapshots":[{"event_id":"sha256:445c2e169ad39d823b671b03d843fb0e292489d12209e4842166bf30d47f723c","target":"graph","created_at":"2026-07-05T03:34:02Z","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/2111.11366/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks (DNNs) have recently achieved a great success in computer vision and several related fields. Despite such progress, current neural architectures still suffer from catastrophic interference (a.k.a. forgetting) which obstructs DNNs to learn continually. While several state-of-the-art methods have been proposed to mitigate forgetting, these existing solutions are either highly rigid (as regularization) or time/memory demanding (as replay). An intermediate class of methods, based on dynamic networks, has been proposed in the literature and provides a reasonable balance between","authors_text":"Haoming Zhan, Hichem Sahbi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-22T17:23:34Z","title":"FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.11366","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":"sha256:455b14b88038dfa121bece4a2236c0969d007c8a169138d3811ac27d4ec0f6b2","target":"record","created_at":"2026-07-05T03:34:02Z","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":"257804c0575dd9112c2b6a7e9eb6b262a55282135845b838083dc598b0cfd5d1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-22T17:23:34Z","title_canon_sha256":"10298a4bdac3a47c76b5f9b82c52f8399112e1c34de9059417dbc14b9c46e368"},"schema_version":"1.0","source":{"id":"2111.11366","kind":"arxiv","version":1}},"canonical_sha256":"22f141c831d38da4af20fd7405e8f157e4b94f48aa143f88ca27b0c6bd845cf3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"22f141c831d38da4af20fd7405e8f157e4b94f48aa143f88ca27b0c6bd845cf3","first_computed_at":"2026-07-05T03:34:02.854545Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:34:02.854545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"64VxXRpupseOCINw6JXiwoPgJONTOawVEpGdoQ3+jtTkR+s9PxyjAiWIcbkd/ENEo4LPR16LrEqHNzBxM2/qBw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:34:02.855100Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.11366","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:455b14b88038dfa121bece4a2236c0969d007c8a169138d3811ac27d4ec0f6b2","sha256:445c2e169ad39d823b671b03d843fb0e292489d12209e4842166bf30d47f723c"],"state_sha256":"85ac90cfc8ce08e34a3de52d73d0fa68b28ef287fd79c593b317b0aeff4fbd01"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Uc9g+qy/SKj2xtQJS3loBe15WJXa6GW/yxHZffu8/xaaLIxCsnHYFWp8YQmOpRHa76GrvNhEYRKZLPZuaBExAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T23:52:18.698984Z","bundle_sha256":"67978592761e64cd93f0825bdf21fcbcc8597968b597d38ab1b1bad23c8fea5f"}}