{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:U726PJDQ5NQPX4GFKAOARSPXVP","short_pith_number":"pith:U726PJDQ","schema_version":"1.0","canonical_sha256":"a7f5e7a470eb60fbf0c5501c08c9f7abc0d3b0d75143d8935950958c64810e3b","source":{"kind":"arxiv","id":"1705.08690","version":3},"attestation_state":"computed","paper":{"title":"Continual Learning with Deep Generative Replay","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.AI","authors_text":"Hanul Shin, Jaehong Kim, Jiwon Kim, Jung Kwon Lee","submitted_at":"2017-05-24T10:37:38Z","abstract_excerpt":"Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model (\"gener"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1705.08690","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-05-24T10:37:38Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"742989c6c52f4d5aae91c8b0772164e5b5f972c45044d6034e2841ee435b5207","abstract_canon_sha256":"64757d4128985f9192cc1600898248acbb1a24d7295cc34663ff31d0545a06f7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:15.621769Z","signature_b64":"eSOZW137nwB7V2zCAQEeZyHP87kIcfVtHR1wRKlFiNaEtQljhSrgVLQcaFR9V9MrpgkW97nUktAOOIgcFeYqBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7f5e7a470eb60fbf0c5501c08c9f7abc0d3b0d75143d8935950958c64810e3b","last_reissued_at":"2026-05-18T00:28:15.620954Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:15.620954Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Continual Learning with Deep Generative Replay","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.AI","authors_text":"Hanul Shin, Jaehong Kim, Jiwon Kim, Jung Kwon Lee","submitted_at":"2017-05-24T10:37:38Z","abstract_excerpt":"Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model (\"gener"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08690","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1705.08690","created_at":"2026-05-18T00:28:15.621086+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.08690v3","created_at":"2026-05-18T00:28:15.621086+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08690","created_at":"2026-05-18T00:28:15.621086+00:00"},{"alias_kind":"pith_short_12","alias_value":"U726PJDQ5NQP","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"U726PJDQ5NQPX4GF","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"U726PJDQ","created_at":"2026-05-18T12:31:46.661854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.13281","citing_title":"Attention to task structure for cognitive flexibility","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U726PJDQ5NQPX4GFKAOARSPXVP","json":"https://pith.science/pith/U726PJDQ5NQPX4GFKAOARSPXVP.json","graph_json":"https://pith.science/api/pith-number/U726PJDQ5NQPX4GFKAOARSPXVP/graph.json","events_json":"https://pith.science/api/pith-number/U726PJDQ5NQPX4GFKAOARSPXVP/events.json","paper":"https://pith.science/paper/U726PJDQ"},"agent_actions":{"view_html":"https://pith.science/pith/U726PJDQ5NQPX4GFKAOARSPXVP","download_json":"https://pith.science/pith/U726PJDQ5NQPX4GFKAOARSPXVP.json","view_paper":"https://pith.science/paper/U726PJDQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.08690&json=true","fetch_graph":"https://pith.science/api/pith-number/U726PJDQ5NQPX4GFKAOARSPXVP/graph.json","fetch_events":"https://pith.science/api/pith-number/U726PJDQ5NQPX4GFKAOARSPXVP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U726PJDQ5NQPX4GFKAOARSPXVP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U726PJDQ5NQPX4GFKAOARSPXVP/action/storage_attestation","attest_author":"https://pith.science/pith/U726PJDQ5NQPX4GFKAOARSPXVP/action/author_attestation","sign_citation":"https://pith.science/pith/U726PJDQ5NQPX4GFKAOARSPXVP/action/citation_signature","submit_replication":"https://pith.science/pith/U726PJDQ5NQPX4GFKAOARSPXVP/action/replication_record"}},"created_at":"2026-05-18T00:28:15.621086+00:00","updated_at":"2026-05-18T00:28:15.621086+00:00"}