{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZTFICTCRU7KHBYEGE3VLLSKKTT","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":"3c86cfd1ecfed80467e4a51bfa9f0a02c790d709c63789f886f4927f9d54a15f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-20T18:59:03Z","title_canon_sha256":"40eea5cd4960ec0fafcd22d63269b235b4597f62bd2da9455dce2b2e44630254"},"schema_version":"1.0","source":{"id":"2412.16156","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.16156","created_at":"2026-07-05T09:52:35Z"},{"alias_kind":"arxiv_version","alias_value":"2412.16156v1","created_at":"2026-07-05T09:52:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16156","created_at":"2026-07-05T09:52:35Z"},{"alias_kind":"pith_short_12","alias_value":"ZTFICTCRU7KH","created_at":"2026-07-05T09:52:35Z"},{"alias_kind":"pith_short_16","alias_value":"ZTFICTCRU7KHBYEG","created_at":"2026-07-05T09:52:35Z"},{"alias_kind":"pith_short_8","alias_value":"ZTFICTCR","created_at":"2026-07-05T09:52:35Z"}],"graph_snapshots":[{"event_id":"sha256:b5adf07e109cc3fd76bccd25462ab4961ac7954665b13829510f0fd1e01b57e8","target":"graph","created_at":"2026-07-05T09:52:35Z","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/2412.16156/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modern vision models excel at general purpose downstream tasks. It is unclear, however, how they may be used for personalized vision tasks, which are both fine-grained and data-scarce. Recent works have successfully applied synthetic data to general-purpose representation learning, while advances in T2I diffusion models have enabled the generation of personalized images from just a few real examples. Here, we explore a potential connection between these ideas, and formalize the challenge of using personalized synthetic data to learn personalized representations, which encode knowledge about an","authors_text":"Julia Chae, Phillip Isola, Sara Beery, Shobhita Sundaram, Yonglong Tian","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-20T18:59:03Z","title":"Personalized Representation from Personalized Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16156","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":"sha256:1b3e81f8cd13c951a1e74f9908d387ee6f03f2e1755458553d1f48790cb9ff54","target":"record","created_at":"2026-07-05T09:52:35Z","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":"3c86cfd1ecfed80467e4a51bfa9f0a02c790d709c63789f886f4927f9d54a15f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-20T18:59:03Z","title_canon_sha256":"40eea5cd4960ec0fafcd22d63269b235b4597f62bd2da9455dce2b2e44630254"},"schema_version":"1.0","source":{"id":"2412.16156","kind":"arxiv","version":1}},"canonical_sha256":"ccca814c51a7d470e08626eab5c94a9cf337135d07b85c02d1684e81d20faf21","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ccca814c51a7d470e08626eab5c94a9cf337135d07b85c02d1684e81d20faf21","first_computed_at":"2026-07-05T09:52:35.882613Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:52:35.882613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BteCC1n9zZDhVTZ2geCD1ZRiYyOfq6ksfPmSWYGMnZzI3SHwAahsPBpYUMWsP5e5BWaIdln5qcChJp5/ddGSAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:52:35.883080Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.16156","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1b3e81f8cd13c951a1e74f9908d387ee6f03f2e1755458553d1f48790cb9ff54","sha256:b5adf07e109cc3fd76bccd25462ab4961ac7954665b13829510f0fd1e01b57e8"],"state_sha256":"f1cad235b17637fd32878241c9f8fbf690cd37aab6b63a232c4e8e6a6220bbcb"}