{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:RWQJZ2D5KAU7TBV6B2KEARYZ4W","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":"a5bdd152b8925819eca6a79099d77e6689dc67216486abdc2670a6bccacc5899","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-27T15:52:14Z","title_canon_sha256":"164d82e8f750e886f1030ad64376ff772e978eaf0088a58de389a45320c91711"},"schema_version":"1.0","source":{"id":"2303.15342","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.15342","created_at":"2026-07-05T05:55:04Z"},{"alias_kind":"arxiv_version","alias_value":"2303.15342v1","created_at":"2026-07-05T05:55:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.15342","created_at":"2026-07-05T05:55:04Z"},{"alias_kind":"pith_short_12","alias_value":"RWQJZ2D5KAU7","created_at":"2026-07-05T05:55:04Z"},{"alias_kind":"pith_short_16","alias_value":"RWQJZ2D5KAU7TBV6","created_at":"2026-07-05T05:55:04Z"},{"alias_kind":"pith_short_8","alias_value":"RWQJZ2D5","created_at":"2026-07-05T05:55:04Z"}],"graph_snapshots":[{"event_id":"sha256:732c35360a8f0472060b4dd9fef7175383ad0d5da3c8e358783a02031ea57ea8","target":"graph","created_at":"2026-07-05T05:55:04Z","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/2303.15342/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied to unprecedented amounts of data. However, training a diffusion model from scratch is computationally expensive. This highlights the need to investigate the possibility of training these models iteratively, reusing computation while the data distribution changes. In this study, we take the first step in this direction and evaluate the continual learning (CL) properties of diffusion models. We begin by benchmarking the most common CL methods applied to Denoising ","authors_text":"Anna Kuzina, Florian Shkurti, Jakub M. Tomczak, Kamil Deja, Micha{\\l} Zaj\\k{a}c, Piotr Mi{\\l}o\\'s, Tomasz Trzci\\'nski","cross_cats":["cs.AI","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-27T15:52:14Z","title":"Exploring Continual Learning of Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.15342","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":"sha256:2045e8ce18b5054ab32f4ef0b9096f60ec6925ade6a5c1f7bd81fc99542ee2ce","target":"record","created_at":"2026-07-05T05:55:04Z","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":"a5bdd152b8925819eca6a79099d77e6689dc67216486abdc2670a6bccacc5899","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-27T15:52:14Z","title_canon_sha256":"164d82e8f750e886f1030ad64376ff772e978eaf0088a58de389a45320c91711"},"schema_version":"1.0","source":{"id":"2303.15342","kind":"arxiv","version":1}},"canonical_sha256":"8da09ce87d5029f986be0e94404719e584e83f727e6ee1d63d8c960857951b21","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8da09ce87d5029f986be0e94404719e584e83f727e6ee1d63d8c960857951b21","first_computed_at":"2026-07-05T05:55:04.067103Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:55:04.067103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iQ3EsI0LHGLxCz7WSg/DKe/Pk60OCLkIxFm6gpcxrn0vR/QlnSM/2QZzKb94kFFmbdL1xI42uJJAak7meGhXCA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:55:04.067556Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.15342","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2045e8ce18b5054ab32f4ef0b9096f60ec6925ade6a5c1f7bd81fc99542ee2ce","sha256:732c35360a8f0472060b4dd9fef7175383ad0d5da3c8e358783a02031ea57ea8"],"state_sha256":"b143477e5c4e3965a5e2a16aab24dcc3e6807f93303394dd8e1fbb91a3fc98b3"}