{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FWIJWBBAY7DK2YYW2ZEO6TJL3U","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":"08ed39927cc9c6b010b49ed6ae999ed4095d3601f92abd0bab7bad23bb6de2c7","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-24T09:47:31Z","title_canon_sha256":"9a63f11a224bd4c6ba096632b5a547793f26256d1d878f044a407b6531f26c69"},"schema_version":"1.0","source":{"id":"2506.19465","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.19465","created_at":"2026-07-05T11:26:15Z"},{"alias_kind":"arxiv_version","alias_value":"2506.19465v1","created_at":"2026-07-05T11:26:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.19465","created_at":"2026-07-05T11:26:15Z"},{"alias_kind":"pith_short_12","alias_value":"FWIJWBBAY7DK","created_at":"2026-07-05T11:26:15Z"},{"alias_kind":"pith_short_16","alias_value":"FWIJWBBAY7DK2YYW","created_at":"2026-07-05T11:26:15Z"},{"alias_kind":"pith_short_8","alias_value":"FWIJWBBA","created_at":"2026-07-05T11:26:15Z"}],"graph_snapshots":[{"event_id":"sha256:0874cec72cad157f9282c16a958bf7ba4e9ee30511011585ecda8503e41645d0","target":"graph","created_at":"2026-07-05T11:26:15Z","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/2506.19465/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modern deep learning models in computer vision require large datasets of real images, which are difficult to curate and pose privacy and legal concerns, limiting their commercial use. Recent works suggest synthetic data as an alternative, yet models trained with it often underperform. This paper proposes a two-step approach to bridge this gap. First, we propose an improved neural fractal formulation through which we introduce a new class of synthetic data. Second, we propose reverse stylization, a technique that transfers visual features from a small, license-free set of real images onto synth","authors_text":"Amirhossein Askari Farsangi, Farnood Salehi, Tun\\c{c} Ozan Ayd{\\i}n, Vandit Sharma","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-24T09:47:31Z","title":"Stylized Structural Patterns for Improved Neural Network Pre-training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.19465","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:ba5ed3dd9fef480e3edaf02a9ce330610ccc3328fe03f5a762f402efe031be45","target":"record","created_at":"2026-07-05T11:26:15Z","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":"08ed39927cc9c6b010b49ed6ae999ed4095d3601f92abd0bab7bad23bb6de2c7","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-24T09:47:31Z","title_canon_sha256":"9a63f11a224bd4c6ba096632b5a547793f26256d1d878f044a407b6531f26c69"},"schema_version":"1.0","source":{"id":"2506.19465","kind":"arxiv","version":1}},"canonical_sha256":"2d909b0420c7c6ad6316d648ef4d2bdd1f849e9d5ae048c070e5aa757a0b1fd4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2d909b0420c7c6ad6316d648ef4d2bdd1f849e9d5ae048c070e5aa757a0b1fd4","first_computed_at":"2026-07-05T11:26:15.441904Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:26:15.441904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x8aK7T+c8734Pek05bgDQET0yFtZQlitT2Q59M94LQkRD8/RdVQEnVWzH2/i+f71+JmVpVSXnDu7XeY4bsZKAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:26:15.442424Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.19465","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ba5ed3dd9fef480e3edaf02a9ce330610ccc3328fe03f5a762f402efe031be45","sha256:0874cec72cad157f9282c16a958bf7ba4e9ee30511011585ecda8503e41645d0"],"state_sha256":"065b9e8907398c36515eadefcebe195fd4c8bbb7cd566a713d0a1c65398a5386"}