{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:SCN2C7VHESP64BCXU5Y4L52ENB","short_pith_number":"pith:SCN2C7VH","canonical_record":{"source":{"id":"2212.10229","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-20T13:07:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"042058b7a7f7c78f436cef865f828e7dc1795adad49e64e75f5b401fb110ec5d","abstract_canon_sha256":"6c38909268ad66f68ec682870fd01bfe11997df872f4886251b981481f426825"},"schema_version":"1.0"},"canonical_sha256":"909ba17ea7249fee0457a771c5f744687f4d402f8c797aaa0fe38d5d0241a426","source":{"kind":"arxiv","id":"2212.10229","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.10229","created_at":"2026-07-05T06:49:40Z"},{"alias_kind":"arxiv_version","alias_value":"2212.10229v4","created_at":"2026-07-05T06:49:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10229","created_at":"2026-07-05T06:49:40Z"},{"alias_kind":"pith_short_12","alias_value":"SCN2C7VHESP6","created_at":"2026-07-05T06:49:40Z"},{"alias_kind":"pith_short_16","alias_value":"SCN2C7VHESP64BCX","created_at":"2026-07-05T06:49:40Z"},{"alias_kind":"pith_short_8","alias_value":"SCN2C7VH","created_at":"2026-07-05T06:49:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:SCN2C7VHESP64BCXU5Y4L52ENB","target":"record","payload":{"canonical_record":{"source":{"id":"2212.10229","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-20T13:07:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"042058b7a7f7c78f436cef865f828e7dc1795adad49e64e75f5b401fb110ec5d","abstract_canon_sha256":"6c38909268ad66f68ec682870fd01bfe11997df872f4886251b981481f426825"},"schema_version":"1.0"},"canonical_sha256":"909ba17ea7249fee0457a771c5f744687f4d402f8c797aaa0fe38d5d0241a426","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:49:40.999495Z","signature_b64":"9oF20ARsreQzSE0ButnVndkp9gTzkDxYLZOp3Wy1Si8NjaRLTqpH/3uvjGWlCa5alAsifHzIfqO5qQ893TiLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"909ba17ea7249fee0457a771c5f744687f4d402f8c797aaa0fe38d5d0241a426","last_reissued_at":"2026-07-05T06:49:40.998971Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:49:40.998971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.10229","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:49:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xWq+r8pVuanH9f4CxKKV5LTV9/+6+MRO/631dHh3ZNcVs2Aciy79lC0UVFnAFxReC7sPjFtmNE0uChu9PqZRBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T03:34:04.736388Z"},"content_sha256":"75ffe2700be38a9c3fa4ea75711c8bb010ee303ad77f56c12a523104779c09fc","schema_version":"1.0","event_id":"sha256:75ffe2700be38a9c3fa4ea75711c8bb010ee303ad77f56c12a523104779c09fc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:SCN2C7VHESP64BCXU5Y4L52ENB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Aibek Alanov, Dmitry Vetrov, Maksim Nakhodnov, Vadim Titov","submitted_at":"2022-12-20T13:07:20Z","abstract_excerpt":"Domain adaptation of GANs is a problem of fine-tuning GAN models pretrained on a large dataset (e.g. StyleGAN) to a specific domain with few samples (e.g. painting faces, sketches, etc.). While there are many methods that tackle this problem in different ways, there are still many important questions that remain unanswered. In this paper, we provide a systematic and in-depth analysis of the domain adaptation problem of GANs, focusing on the StyleGAN model. We perform a detailed exploration of the most important parts of StyleGAN that are responsible for adapting the generator to a new domain d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10229","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2212.10229/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:49:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2LpydGQ7t6oqw8VSauSQ/ADimGD1hZOrZqTDTDbreFYjqnybD6xOVUr8uf4qUb6dPORpCwhjhmZ5JBkJnGpgDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T03:34:04.736784Z"},"content_sha256":"54474266b02eaa93141cef63ec3f8c314dcd585dca52ba69c697b0ea880f79a6","schema_version":"1.0","event_id":"sha256:54474266b02eaa93141cef63ec3f8c314dcd585dca52ba69c697b0ea880f79a6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SCN2C7VHESP64BCXU5Y4L52ENB/bundle.json","state_url":"https://pith.science/pith/SCN2C7VHESP64BCXU5Y4L52ENB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SCN2C7VHESP64BCXU5Y4L52ENB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-12T03:34:04Z","links":{"resolver":"https://pith.science/pith/SCN2C7VHESP64BCXU5Y4L52ENB","bundle":"https://pith.science/pith/SCN2C7VHESP64BCXU5Y4L52ENB/bundle.json","state":"https://pith.science/pith/SCN2C7VHESP64BCXU5Y4L52ENB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SCN2C7VHESP64BCXU5Y4L52ENB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:SCN2C7VHESP64BCXU5Y4L52ENB","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":"6c38909268ad66f68ec682870fd01bfe11997df872f4886251b981481f426825","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-20T13:07:20Z","title_canon_sha256":"042058b7a7f7c78f436cef865f828e7dc1795adad49e64e75f5b401fb110ec5d"},"schema_version":"1.0","source":{"id":"2212.10229","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.10229","created_at":"2026-07-05T06:49:40Z"},{"alias_kind":"arxiv_version","alias_value":"2212.10229v4","created_at":"2026-07-05T06:49:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10229","created_at":"2026-07-05T06:49:40Z"},{"alias_kind":"pith_short_12","alias_value":"SCN2C7VHESP6","created_at":"2026-07-05T06:49:40Z"},{"alias_kind":"pith_short_16","alias_value":"SCN2C7VHESP64BCX","created_at":"2026-07-05T06:49:40Z"},{"alias_kind":"pith_short_8","alias_value":"SCN2C7VH","created_at":"2026-07-05T06:49:40Z"}],"graph_snapshots":[{"event_id":"sha256:54474266b02eaa93141cef63ec3f8c314dcd585dca52ba69c697b0ea880f79a6","target":"graph","created_at":"2026-07-05T06:49:40Z","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/2212.10229/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Domain adaptation of GANs is a problem of fine-tuning GAN models pretrained on a large dataset (e.g. StyleGAN) to a specific domain with few samples (e.g. painting faces, sketches, etc.). While there are many methods that tackle this problem in different ways, there are still many important questions that remain unanswered. In this paper, we provide a systematic and in-depth analysis of the domain adaptation problem of GANs, focusing on the StyleGAN model. We perform a detailed exploration of the most important parts of StyleGAN that are responsible for adapting the generator to a new domain d","authors_text":"Aibek Alanov, Dmitry Vetrov, Maksim Nakhodnov, Vadim Titov","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-20T13:07:20Z","title":"StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10229","kind":"arxiv","version":4},"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":"sha256:75ffe2700be38a9c3fa4ea75711c8bb010ee303ad77f56c12a523104779c09fc","target":"record","created_at":"2026-07-05T06:49:40Z","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":"6c38909268ad66f68ec682870fd01bfe11997df872f4886251b981481f426825","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-20T13:07:20Z","title_canon_sha256":"042058b7a7f7c78f436cef865f828e7dc1795adad49e64e75f5b401fb110ec5d"},"schema_version":"1.0","source":{"id":"2212.10229","kind":"arxiv","version":4}},"canonical_sha256":"909ba17ea7249fee0457a771c5f744687f4d402f8c797aaa0fe38d5d0241a426","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"909ba17ea7249fee0457a771c5f744687f4d402f8c797aaa0fe38d5d0241a426","first_computed_at":"2026-07-05T06:49:40.998971Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:49:40.998971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9oF20ARsreQzSE0ButnVndkp9gTzkDxYLZOp3Wy1Si8NjaRLTqpH/3uvjGWlCa5alAsifHzIfqO5qQ893TiLCw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:49:40.999495Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.10229","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75ffe2700be38a9c3fa4ea75711c8bb010ee303ad77f56c12a523104779c09fc","sha256:54474266b02eaa93141cef63ec3f8c314dcd585dca52ba69c697b0ea880f79a6"],"state_sha256":"c396becf14e3be7a07cc1f0325a3b8951bdceb1197e5d88380e8529efe95c3ca"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BHFXZQD5Wz8KSEVjYybaZostB81PSO/efhU5JRkltJMHQ3bPyDWdRkY/jHps9zFjaFI5ZiG4swpFVL6e5NVvBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-12T03:34:04.739355Z","bundle_sha256":"943dba2a2725982e2c4bf9671ffdf12c2ceafc1d431ff89a48e34451f3fcaed9"}}