{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:JUVR3YEU732K25SPXUYYJFCHWF","short_pith_number":"pith:JUVR3YEU","canonical_record":{"source":{"id":"2308.10554","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-21T08:12:28Z","cross_cats_sorted":[],"title_canon_sha256":"39986a54bb291d5bd1c4e6f92e65f102f059d0cc9604e0b10c1800884d7971ee","abstract_canon_sha256":"cffef742d9c92c6d87f53f4ce7810581e2bc026053d272e073ad81799eed8a57"},"schema_version":"1.0"},"canonical_sha256":"4d2b1de094fef4ad764fbd31849447b14cc57a66925d70fc3a106260cd99d19f","source":{"kind":"arxiv","id":"2308.10554","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.10554","created_at":"2026-07-05T06:43:11Z"},{"alias_kind":"arxiv_version","alias_value":"2308.10554v1","created_at":"2026-07-05T06:43:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.10554","created_at":"2026-07-05T06:43:11Z"},{"alias_kind":"pith_short_12","alias_value":"JUVR3YEU732K","created_at":"2026-07-05T06:43:11Z"},{"alias_kind":"pith_short_16","alias_value":"JUVR3YEU732K25SP","created_at":"2026-07-05T06:43:11Z"},{"alias_kind":"pith_short_8","alias_value":"JUVR3YEU","created_at":"2026-07-05T06:43:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:JUVR3YEU732K25SPXUYYJFCHWF","target":"record","payload":{"canonical_record":{"source":{"id":"2308.10554","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-21T08:12:28Z","cross_cats_sorted":[],"title_canon_sha256":"39986a54bb291d5bd1c4e6f92e65f102f059d0cc9604e0b10c1800884d7971ee","abstract_canon_sha256":"cffef742d9c92c6d87f53f4ce7810581e2bc026053d272e073ad81799eed8a57"},"schema_version":"1.0"},"canonical_sha256":"4d2b1de094fef4ad764fbd31849447b14cc57a66925d70fc3a106260cd99d19f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:43:11.031857Z","signature_b64":"yc/YnJo8N28i6z3U3AH2DwV8VTxphkRpxH8NVxODocklUmZgJhgWqOD01qbtWfmBXpYjSS5ni7RzJug3bXouAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d2b1de094fef4ad764fbd31849447b14cc57a66925d70fc3a106260cd99d19f","last_reissued_at":"2026-07-05T06:43:11.031362Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:43:11.031362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.10554","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":"2026-07-05T06:43:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uSqxrrcTPD88nwzUdbDsos0wEwnt5ADyUR4qH2uJB+FV1p2SRvffalsL1YREW4ZzK719+Ff2aLHC/QJMzN4CDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T01:04:17.220863Z"},"content_sha256":"2e95fa501e89a4c4ecb1d9e91d4215de4c6c52839f9cba2da6e3aeea187bf6d5","schema_version":"1.0","event_id":"sha256:2e95fa501e89a4c4ecb1d9e91d4215de4c6c52839f9cba2da6e3aeea187bf6d5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:JUVR3YEU732K25SPXUYYJFCHWF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bei Liu, Hyeran Byun, Jianlong Fu, Kibeom Hong, Pilhyeon Lee, Seogkyu Jeon","submitted_at":"2023-08-21T08:12:28Z","abstract_excerpt":"Training deep generative models usually requires a large amount of data. To alleviate the data collection cost, the task of zero-shot GAN adaptation aims to reuse well-trained generators to synthesize images of an unseen target domain without any further training samples. Due to the data absence, the textual description of the target domain and the vision-language models, e.g., CLIP, are utilized to effectively guide the generator. However, with only a single representative text feature instead of real images, the synthesized images gradually lose diversity as the model is optimized, which is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.10554","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/2308.10554/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":"2026-07-05T06:43:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V/ZDgb7QpQhcgmXUTRjuisTtJIdtSScQVHotTGbsgGBJ4fu+4uY2NZaxxWR7KDwFdUIp5awGL/aBTTWIeatfBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T01:04:17.221364Z"},"content_sha256":"be6411d98537a30874a52a112fc9f59b39766ed4d67dafb7132a830870250e3a","schema_version":"1.0","event_id":"sha256:be6411d98537a30874a52a112fc9f59b39766ed4d67dafb7132a830870250e3a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JUVR3YEU732K25SPXUYYJFCHWF/bundle.json","state_url":"https://pith.science/pith/JUVR3YEU732K25SPXUYYJFCHWF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JUVR3YEU732K25SPXUYYJFCHWF/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":"2026-08-05T01:04:17Z","links":{"resolver":"https://pith.science/pith/JUVR3YEU732K25SPXUYYJFCHWF","bundle":"https://pith.science/pith/JUVR3YEU732K25SPXUYYJFCHWF/bundle.json","state":"https://pith.science/pith/JUVR3YEU732K25SPXUYYJFCHWF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JUVR3YEU732K25SPXUYYJFCHWF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:JUVR3YEU732K25SPXUYYJFCHWF","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":"cffef742d9c92c6d87f53f4ce7810581e2bc026053d272e073ad81799eed8a57","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-21T08:12:28Z","title_canon_sha256":"39986a54bb291d5bd1c4e6f92e65f102f059d0cc9604e0b10c1800884d7971ee"},"schema_version":"1.0","source":{"id":"2308.10554","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.10554","created_at":"2026-07-05T06:43:11Z"},{"alias_kind":"arxiv_version","alias_value":"2308.10554v1","created_at":"2026-07-05T06:43:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.10554","created_at":"2026-07-05T06:43:11Z"},{"alias_kind":"pith_short_12","alias_value":"JUVR3YEU732K","created_at":"2026-07-05T06:43:11Z"},{"alias_kind":"pith_short_16","alias_value":"JUVR3YEU732K25SP","created_at":"2026-07-05T06:43:11Z"},{"alias_kind":"pith_short_8","alias_value":"JUVR3YEU","created_at":"2026-07-05T06:43:11Z"}],"graph_snapshots":[{"event_id":"sha256:be6411d98537a30874a52a112fc9f59b39766ed4d67dafb7132a830870250e3a","target":"graph","created_at":"2026-07-05T06:43:11Z","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/2308.10554/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Training deep generative models usually requires a large amount of data. To alleviate the data collection cost, the task of zero-shot GAN adaptation aims to reuse well-trained generators to synthesize images of an unseen target domain without any further training samples. Due to the data absence, the textual description of the target domain and the vision-language models, e.g., CLIP, are utilized to effectively guide the generator. However, with only a single representative text feature instead of real images, the synthesized images gradually lose diversity as the model is optimized, which is ","authors_text":"Bei Liu, Hyeran Byun, Jianlong Fu, Kibeom Hong, Pilhyeon Lee, Seogkyu Jeon","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-21T08:12:28Z","title":"Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.10554","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":"sha256:2e95fa501e89a4c4ecb1d9e91d4215de4c6c52839f9cba2da6e3aeea187bf6d5","target":"record","created_at":"2026-07-05T06:43:11Z","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":"cffef742d9c92c6d87f53f4ce7810581e2bc026053d272e073ad81799eed8a57","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-21T08:12:28Z","title_canon_sha256":"39986a54bb291d5bd1c4e6f92e65f102f059d0cc9604e0b10c1800884d7971ee"},"schema_version":"1.0","source":{"id":"2308.10554","kind":"arxiv","version":1}},"canonical_sha256":"4d2b1de094fef4ad764fbd31849447b14cc57a66925d70fc3a106260cd99d19f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4d2b1de094fef4ad764fbd31849447b14cc57a66925d70fc3a106260cd99d19f","first_computed_at":"2026-07-05T06:43:11.031362Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:43:11.031362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yc/YnJo8N28i6z3U3AH2DwV8VTxphkRpxH8NVxODocklUmZgJhgWqOD01qbtWfmBXpYjSS5ni7RzJug3bXouAg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:43:11.031857Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.10554","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2e95fa501e89a4c4ecb1d9e91d4215de4c6c52839f9cba2da6e3aeea187bf6d5","sha256:be6411d98537a30874a52a112fc9f59b39766ed4d67dafb7132a830870250e3a"],"state_sha256":"10c44e434638e25f1d158928ea0222efdcc0315fcd32389e64545e356af37205"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u8t9mDrzi86gnHfW8RY9N5PXE4Horwb5+hhVfERpSfhSunMuyvHF0X97cWhiRYkV69RCTs7rTHO0eFAqxNLfBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T01:04:17.226074Z","bundle_sha256":"7f0f55a11b3beb6cfbdb88adf452e22fd17149c5a5ae49179b075d37216649c9"}}