{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:7K4GITEHBRHGQYYA4OFXN4UGRF","short_pith_number":"pith:7K4GITEH","canonical_record":{"source":{"id":"1808.06396","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-20T11:39:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"07a332a270d445645f8c21750ecc7b1a67c604e7223f515ae71cd1f704982e84","abstract_canon_sha256":"86367a6bfcfa76709c42a2c3eb579cdfb042802de2294574d68ecfdef6d37c32"},"schema_version":"1.0"},"canonical_sha256":"fab8644c870c4e686300e38b76f28689563e6a6588b2a92ee21f276a7b1ea20c","source":{"kind":"arxiv","id":"1808.06396","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.06396","created_at":"2026-05-18T00:07:45Z"},{"alias_kind":"arxiv_version","alias_value":"1808.06396v1","created_at":"2026-05-18T00:07:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06396","created_at":"2026-05-18T00:07:45Z"},{"alias_kind":"pith_short_12","alias_value":"7K4GITEHBRHG","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7K4GITEHBRHGQYYA","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7K4GITEH","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:7K4GITEHBRHGQYYA4OFXN4UGRF","target":"record","payload":{"canonical_record":{"source":{"id":"1808.06396","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-20T11:39:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"07a332a270d445645f8c21750ecc7b1a67c604e7223f515ae71cd1f704982e84","abstract_canon_sha256":"86367a6bfcfa76709c42a2c3eb579cdfb042802de2294574d68ecfdef6d37c32"},"schema_version":"1.0"},"canonical_sha256":"fab8644c870c4e686300e38b76f28689563e6a6588b2a92ee21f276a7b1ea20c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:45.100556Z","signature_b64":"rPsOtSLpW6sYeOvuyRQzDgsH9Y9fUqV0YLjJtvezb3wSXMEN7SdqrDhVq+z+7lL3SKOyIfkUOQRP3G6inAnDBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fab8644c870c4e686300e38b76f28689563e6a6588b2a92ee21f276a7b1ea20c","last_reissued_at":"2026-05-18T00:07:45.099903Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:45.099903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.06396","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":"2026-05-18T00:07:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rqcjsigC4MMc0ZRpTT8mMMxJCnLoym6oCnowyfBEY3afZxF4c9N+/wEWfQHBAxfUBvmXZs0hmTl2Y8Arsx1sDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:27:54.443572Z"},"content_sha256":"7bf09afddd2520184a4c8de21f6a50fe34977899023a30de8adf3180a1bf5fec","schema_version":"1.0","event_id":"sha256:7bf09afddd2520184a4c8de21f6a50fe34977899023a30de8adf3180a1bf5fec"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:7K4GITEHBRHGQYYA4OFXN4UGRF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeeSIL: Deep-Shallow Incremental Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Adrian Popescu, Eden Belouadah","submitted_at":"2018-08-20T11:39:09Z","abstract_excerpt":"Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget. This is especially true when IL is modeled using a deep learning approach, where two com- plex challenges arise due to limited memory, which induces catastrophic forgetting and delays related to the retraining needed in order to incorpo- rate new classes. Here we introduce DeeSIL, an adaptation of a known transfer learning scheme that combines a fixed deep representation used as feature extractor and learning independent shallow classifiers to in- crease recognition capacity. This scheme t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06396","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":"2026-05-18T00:07:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tyYYWEaLV8A+lGUe390vJG143VHx8HOdAhDxloi8edhFDmrudN9sGGJ2tQaSnPi/KYnEmBQYXGwjVAJa6FWOAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:27:54.444131Z"},"content_sha256":"0300788f1408c594765822b4dc7be9b0302bc53150882a6948c4a80d66504a7e","schema_version":"1.0","event_id":"sha256:0300788f1408c594765822b4dc7be9b0302bc53150882a6948c4a80d66504a7e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7K4GITEHBRHGQYYA4OFXN4UGRF/bundle.json","state_url":"https://pith.science/pith/7K4GITEHBRHGQYYA4OFXN4UGRF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7K4GITEHBRHGQYYA4OFXN4UGRF/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":"2026-05-25T21:27:54Z","links":{"resolver":"https://pith.science/pith/7K4GITEHBRHGQYYA4OFXN4UGRF","bundle":"https://pith.science/pith/7K4GITEHBRHGQYYA4OFXN4UGRF/bundle.json","state":"https://pith.science/pith/7K4GITEHBRHGQYYA4OFXN4UGRF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7K4GITEHBRHGQYYA4OFXN4UGRF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:7K4GITEHBRHGQYYA4OFXN4UGRF","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":"86367a6bfcfa76709c42a2c3eb579cdfb042802de2294574d68ecfdef6d37c32","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-20T11:39:09Z","title_canon_sha256":"07a332a270d445645f8c21750ecc7b1a67c604e7223f515ae71cd1f704982e84"},"schema_version":"1.0","source":{"id":"1808.06396","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.06396","created_at":"2026-05-18T00:07:45Z"},{"alias_kind":"arxiv_version","alias_value":"1808.06396v1","created_at":"2026-05-18T00:07:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06396","created_at":"2026-05-18T00:07:45Z"},{"alias_kind":"pith_short_12","alias_value":"7K4GITEHBRHG","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7K4GITEHBRHGQYYA","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7K4GITEH","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:0300788f1408c594765822b4dc7be9b0302bc53150882a6948c4a80d66504a7e","target":"graph","created_at":"2026-05-18T00:07:45Z","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":"Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget. This is especially true when IL is modeled using a deep learning approach, where two com- plex challenges arise due to limited memory, which induces catastrophic forgetting and delays related to the retraining needed in order to incorpo- rate new classes. Here we introduce DeeSIL, an adaptation of a known transfer learning scheme that combines a fixed deep representation used as feature extractor and learning independent shallow classifiers to in- crease recognition capacity. This scheme t","authors_text":"Adrian Popescu, Eden Belouadah","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-20T11:39:09Z","title":"DeeSIL: Deep-Shallow Incremental Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06396","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":"sha256:7bf09afddd2520184a4c8de21f6a50fe34977899023a30de8adf3180a1bf5fec","target":"record","created_at":"2026-05-18T00:07:45Z","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":"86367a6bfcfa76709c42a2c3eb579cdfb042802de2294574d68ecfdef6d37c32","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-20T11:39:09Z","title_canon_sha256":"07a332a270d445645f8c21750ecc7b1a67c604e7223f515ae71cd1f704982e84"},"schema_version":"1.0","source":{"id":"1808.06396","kind":"arxiv","version":1}},"canonical_sha256":"fab8644c870c4e686300e38b76f28689563e6a6588b2a92ee21f276a7b1ea20c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fab8644c870c4e686300e38b76f28689563e6a6588b2a92ee21f276a7b1ea20c","first_computed_at":"2026-05-18T00:07:45.099903Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:45.099903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rPsOtSLpW6sYeOvuyRQzDgsH9Y9fUqV0YLjJtvezb3wSXMEN7SdqrDhVq+z+7lL3SKOyIfkUOQRP3G6inAnDBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:45.100556Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.06396","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7bf09afddd2520184a4c8de21f6a50fe34977899023a30de8adf3180a1bf5fec","sha256:0300788f1408c594765822b4dc7be9b0302bc53150882a6948c4a80d66504a7e"],"state_sha256":"e00b723f94b13a7b89ed33f1e10aaf5840750122142b5a6542743379462a08a4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iDIrTvIEIxVnYhuUbJHDt5CzsmEjXJnAPjBGmV3IWM7U/oMSZmRAI12yzywG6O9jb1MdSbBrPBX3dLjfmchTDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:27:54.447381Z","bundle_sha256":"99d448e360d9c0603fb4b0b3eef2199c586b42f17bf5481036eb783efebe9aeb"}}