{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:3JUYU2AJU2REMKTVX54KJAFJPL","short_pith_number":"pith:3JUYU2AJ","canonical_record":{"source":{"id":"2202.12211","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-02-24T17:16:47Z","cross_cats_sorted":[],"title_canon_sha256":"55840327d8ad99b4e08c56b881fe57b831603cbfd23cbc98e9738e86633f943c","abstract_canon_sha256":"15a04e377033c34fc50a0e38e77a37b9502b4f2219c9f8e59f5e80d2e5f340fb"},"schema_version":"1.0"},"canonical_sha256":"da698a6809a6a2462a75bf78a480a97aeb6ce84b2be79acf99dfaea80ada44ae","source":{"kind":"arxiv","id":"2202.12211","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.12211","created_at":"2026-07-05T03:59:51Z"},{"alias_kind":"arxiv_version","alias_value":"2202.12211v1","created_at":"2026-07-05T03:59:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.12211","created_at":"2026-07-05T03:59:51Z"},{"alias_kind":"pith_short_12","alias_value":"3JUYU2AJU2RE","created_at":"2026-07-05T03:59:51Z"},{"alias_kind":"pith_short_16","alias_value":"3JUYU2AJU2REMKTV","created_at":"2026-07-05T03:59:51Z"},{"alias_kind":"pith_short_8","alias_value":"3JUYU2AJ","created_at":"2026-07-05T03:59:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:3JUYU2AJU2REMKTVX54KJAFJPL","target":"record","payload":{"canonical_record":{"source":{"id":"2202.12211","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-02-24T17:16:47Z","cross_cats_sorted":[],"title_canon_sha256":"55840327d8ad99b4e08c56b881fe57b831603cbfd23cbc98e9738e86633f943c","abstract_canon_sha256":"15a04e377033c34fc50a0e38e77a37b9502b4f2219c9f8e59f5e80d2e5f340fb"},"schema_version":"1.0"},"canonical_sha256":"da698a6809a6a2462a75bf78a480a97aeb6ce84b2be79acf99dfaea80ada44ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:59:51.048935Z","signature_b64":"MFD7S5Pp1vpDnFni7Xo56VIRn6kIPYcrUQv2qS5mRG8gBPUqW4CBjDwPCmQxdA0BskqfjnCLWAnHjWatCwqSAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da698a6809a6a2462a75bf78a480a97aeb6ce84b2be79acf99dfaea80ada44ae","last_reissued_at":"2026-07-05T03:59:51.048553Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:59:51.048553Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.12211","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:59:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YaTgZUKESWiki1WFaMWtctkAhWdPCeghfBvirJo0tFeMZEfJR5vz9AAEk3spcI6ovixBUbWFhH/cXFcJLvSUDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T12:28:51.932680Z"},"content_sha256":"d46541c89e85dc67f27bd65422f4d1b008b69f4e88998af43ccccacea572e889","schema_version":"1.0","event_id":"sha256:d46541c89e85dc67f27bd65422f4d1b008b69f4e88998af43ccccacea572e889"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:3JUYU2AJU2REMKTVX54KJAFJPL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-Distilled StyleGAN: Towards Generation from Internet Photos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daniel Cohen-Or, Inbar Mosseri, Michal Irani, Michal Yarom, Omer Tov, Oran Lang, Ron Mokady, Tali Dekel","submitted_at":"2022-02-24T17:16:47Z","abstract_excerpt":"StyleGAN is known to produce high-fidelity images, while also offering unprecedented semantic editing. However, these fascinating abilities have been demonstrated only on a limited set of datasets, which are usually structurally aligned and well curated. In this paper, we show how StyleGAN can be adapted to work on raw uncurated images collected from the Internet. Such image collections impose two main challenges to StyleGAN: they contain many outlier images, and are characterized by a multi-modal distribution. Training StyleGAN on such raw image collections results in degraded image synthesis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.12211","kind":"arxiv","version":1},"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/2202.12211/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":"2026-07-05T03:59:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8b5ZyrWikhJN97p0ChkWDcdkgHdA3JarOIR6cdaM1HrnM+NDUqiNnpP1WLWzihWMKf6uyuaIKKqbJHIirnQPBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T12:28:51.933180Z"},"content_sha256":"92f7a4779ca015f3ea316c71b94c5e2d0aa2e548c56bd923a0d063dc91f8667a","schema_version":"1.0","event_id":"sha256:92f7a4779ca015f3ea316c71b94c5e2d0aa2e548c56bd923a0d063dc91f8667a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3JUYU2AJU2REMKTVX54KJAFJPL/bundle.json","state_url":"https://pith.science/pith/3JUYU2AJU2REMKTVX54KJAFJPL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3JUYU2AJU2REMKTVX54KJAFJPL/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":"2026-08-21T12:28:51Z","links":{"resolver":"https://pith.science/pith/3JUYU2AJU2REMKTVX54KJAFJPL","bundle":"https://pith.science/pith/3JUYU2AJU2REMKTVX54KJAFJPL/bundle.json","state":"https://pith.science/pith/3JUYU2AJU2REMKTVX54KJAFJPL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3JUYU2AJU2REMKTVX54KJAFJPL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:3JUYU2AJU2REMKTVX54KJAFJPL","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":"15a04e377033c34fc50a0e38e77a37b9502b4f2219c9f8e59f5e80d2e5f340fb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-02-24T17:16:47Z","title_canon_sha256":"55840327d8ad99b4e08c56b881fe57b831603cbfd23cbc98e9738e86633f943c"},"schema_version":"1.0","source":{"id":"2202.12211","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.12211","created_at":"2026-07-05T03:59:51Z"},{"alias_kind":"arxiv_version","alias_value":"2202.12211v1","created_at":"2026-07-05T03:59:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.12211","created_at":"2026-07-05T03:59:51Z"},{"alias_kind":"pith_short_12","alias_value":"3JUYU2AJU2RE","created_at":"2026-07-05T03:59:51Z"},{"alias_kind":"pith_short_16","alias_value":"3JUYU2AJU2REMKTV","created_at":"2026-07-05T03:59:51Z"},{"alias_kind":"pith_short_8","alias_value":"3JUYU2AJ","created_at":"2026-07-05T03:59:51Z"}],"graph_snapshots":[{"event_id":"sha256:92f7a4779ca015f3ea316c71b94c5e2d0aa2e548c56bd923a0d063dc91f8667a","target":"graph","created_at":"2026-07-05T03:59:51Z","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/2202.12211/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"StyleGAN is known to produce high-fidelity images, while also offering unprecedented semantic editing. However, these fascinating abilities have been demonstrated only on a limited set of datasets, which are usually structurally aligned and well curated. In this paper, we show how StyleGAN can be adapted to work on raw uncurated images collected from the Internet. Such image collections impose two main challenges to StyleGAN: they contain many outlier images, and are characterized by a multi-modal distribution. Training StyleGAN on such raw image collections results in degraded image synthesis","authors_text":"Daniel Cohen-Or, Inbar Mosseri, Michal Irani, Michal Yarom, Omer Tov, Oran Lang, Ron Mokady, Tali Dekel","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-02-24T17:16:47Z","title":"Self-Distilled StyleGAN: Towards Generation from Internet Photos"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.12211","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:d46541c89e85dc67f27bd65422f4d1b008b69f4e88998af43ccccacea572e889","target":"record","created_at":"2026-07-05T03:59:51Z","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":"15a04e377033c34fc50a0e38e77a37b9502b4f2219c9f8e59f5e80d2e5f340fb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-02-24T17:16:47Z","title_canon_sha256":"55840327d8ad99b4e08c56b881fe57b831603cbfd23cbc98e9738e86633f943c"},"schema_version":"1.0","source":{"id":"2202.12211","kind":"arxiv","version":1}},"canonical_sha256":"da698a6809a6a2462a75bf78a480a97aeb6ce84b2be79acf99dfaea80ada44ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"da698a6809a6a2462a75bf78a480a97aeb6ce84b2be79acf99dfaea80ada44ae","first_computed_at":"2026-07-05T03:59:51.048553Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:59:51.048553Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MFD7S5Pp1vpDnFni7Xo56VIRn6kIPYcrUQv2qS5mRG8gBPUqW4CBjDwPCmQxdA0BskqfjnCLWAnHjWatCwqSAw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:59:51.048935Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.12211","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d46541c89e85dc67f27bd65422f4d1b008b69f4e88998af43ccccacea572e889","sha256:92f7a4779ca015f3ea316c71b94c5e2d0aa2e548c56bd923a0d063dc91f8667a"],"state_sha256":"96b94fd7e4fe0e5618f4fff91d55da174cc85d2672f7d42001bd6ebd02200f63"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3bSRXwZITvIA9PvFLFZekDMny5iCx83QIO5F6rHLiAT8imObmgdZMKvSj7len0JTo8hiQ4LJJ8Q9hZWQLDuqCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T12:28:51.937895Z","bundle_sha256":"b78a228068dcbeb60cea38358decf06305750397ab8998f4a14c286cdc402cf6"}}