{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:QEL5ULZ2IM6ENXTSMHMURXRLUF","short_pith_number":"pith:QEL5ULZ2","canonical_record":{"source":{"id":"2407.12279","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-17T03:00:05Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"53663eba1c5f03819630bca082271ac1251267a344e37bfbd5609f45cefd9e16","abstract_canon_sha256":"e280659b10c151d623f26239dea5bc3d0b6113d04d91a9bf3b90c9cb82251832"},"schema_version":"1.0"},"canonical_sha256":"8117da2f3a433c46de7261d948de2ba15f7bc5de481ca8ec5c10452bed603dbb","source":{"kind":"arxiv","id":"2407.12279","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.12279","created_at":"2026-07-05T08:45:02Z"},{"alias_kind":"arxiv_version","alias_value":"2407.12279v1","created_at":"2026-07-05T08:45:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.12279","created_at":"2026-07-05T08:45:02Z"},{"alias_kind":"pith_short_12","alias_value":"QEL5ULZ2IM6E","created_at":"2026-07-05T08:45:02Z"},{"alias_kind":"pith_short_16","alias_value":"QEL5ULZ2IM6ENXTS","created_at":"2026-07-05T08:45:02Z"},{"alias_kind":"pith_short_8","alias_value":"QEL5ULZ2","created_at":"2026-07-05T08:45:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:QEL5ULZ2IM6ENXTSMHMURXRLUF","target":"record","payload":{"canonical_record":{"source":{"id":"2407.12279","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-17T03:00:05Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"53663eba1c5f03819630bca082271ac1251267a344e37bfbd5609f45cefd9e16","abstract_canon_sha256":"e280659b10c151d623f26239dea5bc3d0b6113d04d91a9bf3b90c9cb82251832"},"schema_version":"1.0"},"canonical_sha256":"8117da2f3a433c46de7261d948de2ba15f7bc5de481ca8ec5c10452bed603dbb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:45:02.429331Z","signature_b64":"RXcb1jq51B/qjSDuYR4qDtytvcDVSi9OJzYyW1V2HTsTZ7ELaVzSHZ4POoytSCjBAwkWX2lQcQRrooxJmrfGDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8117da2f3a433c46de7261d948de2ba15f7bc5de481ca8ec5c10452bed603dbb","last_reissued_at":"2026-07-05T08:45:02.428903Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:45:02.428903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.12279","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":"2026-07-05T08:45:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W8y7bhv4DT2gCi/7pJUTzTGWUiAZq/g9XbkB427hb4axT7gufaMXVR5lq4K3eQtLT+wWxtfH4I2wJIMOqPORAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T22:33:05.924043Z"},"content_sha256":"2f19c4263a6f9eecca591de55e3224221b308f5359e7607a1fff1af401a87b6f","schema_version":"1.0","event_id":"sha256:2f19c4263a6f9eecca591de55e3224221b308f5359e7607a1fff1af401a87b6f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:QEL5ULZ2IM6ENXTSMHMURXRLUF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ER-FSL: Experience Replay with Feature Subspace Learning for Online Continual Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Huiwei Lin","submitted_at":"2024-07-17T03:00:05Z","abstract_excerpt":"Online continual learning (OCL) involves deep neural networks retaining knowledge from old data while adapting to new data, which is accessible only once. A critical challenge in OCL is catastrophic forgetting, reflected in reduced model performance on old data. Existing replay-based methods mitigate forgetting by replaying buffered samples from old data and learning current samples of new data. In this work, we dissect existing methods and empirically discover that learning and replaying in the same feature space is not conducive to addressing the forgetting issue. Since the learned features "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.12279","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/2407.12279/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":"2026-07-05T08:45:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xanRyDNAs+7r/m0bAQpknq4XrSmf62yCy2zDdyDzWxxEuyPnmWAeQvD/hvLgR2Ya3+v2WMEmdXG1Cg/3gbswCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T22:33:05.924626Z"},"content_sha256":"63aa39c7195e2dbda263370c4b791b916545ffc40513fb50ad49cd2054a62596","schema_version":"1.0","event_id":"sha256:63aa39c7195e2dbda263370c4b791b916545ffc40513fb50ad49cd2054a62596"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QEL5ULZ2IM6ENXTSMHMURXRLUF/bundle.json","state_url":"https://pith.science/pith/QEL5ULZ2IM6ENXTSMHMURXRLUF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QEL5ULZ2IM6ENXTSMHMURXRLUF/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":"2026-08-05T22:33:05Z","links":{"resolver":"https://pith.science/pith/QEL5ULZ2IM6ENXTSMHMURXRLUF","bundle":"https://pith.science/pith/QEL5ULZ2IM6ENXTSMHMURXRLUF/bundle.json","state":"https://pith.science/pith/QEL5ULZ2IM6ENXTSMHMURXRLUF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QEL5ULZ2IM6ENXTSMHMURXRLUF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QEL5ULZ2IM6ENXTSMHMURXRLUF","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":"e280659b10c151d623f26239dea5bc3d0b6113d04d91a9bf3b90c9cb82251832","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-17T03:00:05Z","title_canon_sha256":"53663eba1c5f03819630bca082271ac1251267a344e37bfbd5609f45cefd9e16"},"schema_version":"1.0","source":{"id":"2407.12279","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.12279","created_at":"2026-07-05T08:45:02Z"},{"alias_kind":"arxiv_version","alias_value":"2407.12279v1","created_at":"2026-07-05T08:45:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.12279","created_at":"2026-07-05T08:45:02Z"},{"alias_kind":"pith_short_12","alias_value":"QEL5ULZ2IM6E","created_at":"2026-07-05T08:45:02Z"},{"alias_kind":"pith_short_16","alias_value":"QEL5ULZ2IM6ENXTS","created_at":"2026-07-05T08:45:02Z"},{"alias_kind":"pith_short_8","alias_value":"QEL5ULZ2","created_at":"2026-07-05T08:45:02Z"}],"graph_snapshots":[{"event_id":"sha256:63aa39c7195e2dbda263370c4b791b916545ffc40513fb50ad49cd2054a62596","target":"graph","created_at":"2026-07-05T08:45:02Z","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/2407.12279/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Online continual learning (OCL) involves deep neural networks retaining knowledge from old data while adapting to new data, which is accessible only once. A critical challenge in OCL is catastrophic forgetting, reflected in reduced model performance on old data. Existing replay-based methods mitigate forgetting by replaying buffered samples from old data and learning current samples of new data. In this work, we dissect existing methods and empirically discover that learning and replaying in the same feature space is not conducive to addressing the forgetting issue. Since the learned features ","authors_text":"Huiwei Lin","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-17T03:00:05Z","title":"ER-FSL: Experience Replay with Feature Subspace Learning for Online Continual Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.12279","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":"sha256:2f19c4263a6f9eecca591de55e3224221b308f5359e7607a1fff1af401a87b6f","target":"record","created_at":"2026-07-05T08:45:02Z","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":"e280659b10c151d623f26239dea5bc3d0b6113d04d91a9bf3b90c9cb82251832","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-17T03:00:05Z","title_canon_sha256":"53663eba1c5f03819630bca082271ac1251267a344e37bfbd5609f45cefd9e16"},"schema_version":"1.0","source":{"id":"2407.12279","kind":"arxiv","version":1}},"canonical_sha256":"8117da2f3a433c46de7261d948de2ba15f7bc5de481ca8ec5c10452bed603dbb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8117da2f3a433c46de7261d948de2ba15f7bc5de481ca8ec5c10452bed603dbb","first_computed_at":"2026-07-05T08:45:02.428903Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:45:02.428903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RXcb1jq51B/qjSDuYR4qDtytvcDVSi9OJzYyW1V2HTsTZ7ELaVzSHZ4POoytSCjBAwkWX2lQcQRrooxJmrfGDw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:45:02.429331Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.12279","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2f19c4263a6f9eecca591de55e3224221b308f5359e7607a1fff1af401a87b6f","sha256:63aa39c7195e2dbda263370c4b791b916545ffc40513fb50ad49cd2054a62596"],"state_sha256":"cda1cddd43a618a8ca9e0a556e3b5e731f40847d3fc90dc65f5ad104334e28b9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fN3J5JpcuzVjLS9l+P3h04ASZ/Gb5576/XxeISrkhp27JXGCYYYldodk+kAUtTGXJ8TvhUxeTz14YxMqslgnDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T22:33:05.929802Z","bundle_sha256":"f896f3dc52ed72c2ef91dc285ff268ba7a6722ff7898489f991462894c8258a0"}}