{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JKIC6D64BPJHCJB6XJH5RHRLDS","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":"cb84b2d069368e862bcd6765297b02b814ffaf64bcb109cc2012ee8cc59d1386","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-06T07:51:13Z","title_canon_sha256":"e48b8eb06f85a850989a992f3fa93113ee090bfa2ec02efdbf271be77e810a4c"},"schema_version":"1.0","source":{"id":"2405.03243","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.03243","created_at":"2026-07-05T08:16:00Z"},{"alias_kind":"arxiv_version","alias_value":"2405.03243v1","created_at":"2026-07-05T08:16:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.03243","created_at":"2026-07-05T08:16:00Z"},{"alias_kind":"pith_short_12","alias_value":"JKIC6D64BPJH","created_at":"2026-07-05T08:16:00Z"},{"alias_kind":"pith_short_16","alias_value":"JKIC6D64BPJHCJB6","created_at":"2026-07-05T08:16:00Z"},{"alias_kind":"pith_short_8","alias_value":"JKIC6D64","created_at":"2026-07-05T08:16:00Z"}],"graph_snapshots":[{"event_id":"sha256:7db0e427e3c88eaf656e2e00ece3d492a70701c60455601e64917b3f6f3465cb","target":"graph","created_at":"2026-07-05T08:16:00Z","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/2405.03243/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generative foundation models like Stable Diffusion comprise a diverse spectrum of knowledge in computer vision with the potential for transfer learning, e.g., via generating data to train student models for downstream tasks. This could circumvent the necessity of collecting labeled real-world data, thereby presenting a form of data-free knowledge distillation. However, the resultant student models show a significant drop in accuracy compared to models trained on real data. We investigate possible causes for this drop and focus on the role of the different layers of the student model. By traini","authors_text":"Christian Medeiros Adriano, Holger Giese, Jan Mathias Koehler, Leonhard Hennicke, Lukas Schott","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-06T07:51:13Z","title":"Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.03243","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:f221c491178cd96031b91986883eefb8d95dbcba887b79ab2b5ddd4723695606","target":"record","created_at":"2026-07-05T08:16:00Z","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":"cb84b2d069368e862bcd6765297b02b814ffaf64bcb109cc2012ee8cc59d1386","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-06T07:51:13Z","title_canon_sha256":"e48b8eb06f85a850989a992f3fa93113ee090bfa2ec02efdbf271be77e810a4c"},"schema_version":"1.0","source":{"id":"2405.03243","kind":"arxiv","version":1}},"canonical_sha256":"4a902f0fdc0bd271243eba4fd89e2b1cb50df30a6a0cc40a7f13d4a47998f7dd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4a902f0fdc0bd271243eba4fd89e2b1cb50df30a6a0cc40a7f13d4a47998f7dd","first_computed_at":"2026-07-05T08:16:00.636169Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:16:00.636169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LrUEr8eZnvYhWHb2x7FwNu001JOsc4rEYTHVvINf5kKnr0CkrQZvld56y/O6NBbnZ1VuVGbYtoyuDoy+dHuODA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:16:00.636582Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.03243","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f221c491178cd96031b91986883eefb8d95dbcba887b79ab2b5ddd4723695606","sha256:7db0e427e3c88eaf656e2e00ece3d492a70701c60455601e64917b3f6f3465cb"],"state_sha256":"1b82abb92f19280d1bc81b326442497b6738ff5f3bc63d5f4a477a1395647d2b"}