{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:A6BJM2JLGDM7AIDQZIYY2I3VN3","short_pith_number":"pith:A6BJM2JL","canonical_record":{"source":{"id":"1906.11880","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-05T17:58:28Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4a293ee61a0967579e9159cbe77507fa582cabb54b22748d35a1cafdc41d50ac","abstract_canon_sha256":"d3e4d00dc54f25ee1c44fb716cf95fccaeaf1af0b1de98e981d4e11cfe7e99be"},"schema_version":"1.0"},"canonical_sha256":"078296692b30d9f02070ca318d23756ee0d61eb29b00db65347b46f193cbd7b1","source":{"kind":"arxiv","id":"1906.11880","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.11880","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"arxiv_version","alias_value":"1906.11880v1","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11880","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"pith_short_12","alias_value":"A6BJM2JLGDM7","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"A6BJM2JLGDM7AIDQ","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"A6BJM2JL","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:A6BJM2JLGDM7AIDQZIYY2I3VN3","target":"record","payload":{"canonical_record":{"source":{"id":"1906.11880","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-05T17:58:28Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4a293ee61a0967579e9159cbe77507fa582cabb54b22748d35a1cafdc41d50ac","abstract_canon_sha256":"d3e4d00dc54f25ee1c44fb716cf95fccaeaf1af0b1de98e981d4e11cfe7e99be"},"schema_version":"1.0"},"canonical_sha256":"078296692b30d9f02070ca318d23756ee0d61eb29b00db65347b46f193cbd7b1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:01.767993Z","signature_b64":"s0u703JFeqCO/3I5BjPpdrMA1lYliyP9v3HO7poFUNA/mOAPlObzGA9fAo7EFxT1s+wkIBNSgV5GrUDUnkehCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"078296692b30d9f02070ca318d23756ee0d61eb29b00db65347b46f193cbd7b1","last_reissued_at":"2026-05-17T23:42:01.767361Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:01.767361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.11880","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":"2026-05-17T23:42:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wyjviKsN6CT6yIAA9R/WZW/XBXUxirp0E+lBcWeP5Fr1Pm2DGAu5d9UmYn1f2y1+9DO0JpT2aLbIQblQRY6QBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T23:34:39.345898Z"},"content_sha256":"e62a368af2da3510f81f91c1406ac83aa3c711ca3e152d76a5488310e95d1a8b","schema_version":"1.0","event_id":"sha256:e62a368af2da3510f81f91c1406ac83aa3c711ca3e152d76a5488310e95d1a8b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:A6BJM2JLGDM7AIDQZIYY2I3VN3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Style Generator Inversion for Image Enhancement and Animation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Aviv Gabbay, Yedid Hoshen","submitted_at":"2019-06-05T17:58:28Z","abstract_excerpt":"One of the main motivations for training high quality image generative models is their potential use as tools for image manipulation. Recently, generative adversarial networks (GANs) have been able to generate images of remarkable quality. Unfortunately, adversarially-trained unconditional generator networks have not been successful as image priors. One of the main requirements for a network to act as a generative image prior, is being able to generate every possible image from the target distribution. Adversarial learning often experiences mode-collapse, which manifests in generators that can"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11880","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":""},"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":"2026-05-17T23:42:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LbQAfnBXESLxZIhUVqpt6uHGv0ChwQd/F6Sqk8lRicgtY8/RR/ULgricA3p190DhS5xeRwZBcBEAYkr/u2TTDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T23:34:39.346676Z"},"content_sha256":"20f236e0d7e3bdf310d02450b65c5bb4f8bc5dbf6aa710a4455bd26b4c69ef6a","schema_version":"1.0","event_id":"sha256:20f236e0d7e3bdf310d02450b65c5bb4f8bc5dbf6aa710a4455bd26b4c69ef6a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A6BJM2JLGDM7AIDQZIYY2I3VN3/bundle.json","state_url":"https://pith.science/pith/A6BJM2JLGDM7AIDQZIYY2I3VN3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A6BJM2JLGDM7AIDQZIYY2I3VN3/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":"2026-05-30T23:34:39Z","links":{"resolver":"https://pith.science/pith/A6BJM2JLGDM7AIDQZIYY2I3VN3","bundle":"https://pith.science/pith/A6BJM2JLGDM7AIDQZIYY2I3VN3/bundle.json","state":"https://pith.science/pith/A6BJM2JLGDM7AIDQZIYY2I3VN3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A6BJM2JLGDM7AIDQZIYY2I3VN3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:A6BJM2JLGDM7AIDQZIYY2I3VN3","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":"d3e4d00dc54f25ee1c44fb716cf95fccaeaf1af0b1de98e981d4e11cfe7e99be","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-05T17:58:28Z","title_canon_sha256":"4a293ee61a0967579e9159cbe77507fa582cabb54b22748d35a1cafdc41d50ac"},"schema_version":"1.0","source":{"id":"1906.11880","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.11880","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"arxiv_version","alias_value":"1906.11880v1","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11880","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"pith_short_12","alias_value":"A6BJM2JLGDM7","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"A6BJM2JLGDM7AIDQ","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"A6BJM2JL","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:20f236e0d7e3bdf310d02450b65c5bb4f8bc5dbf6aa710a4455bd26b4c69ef6a","target":"graph","created_at":"2026-05-17T23:42:01Z","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"},"paper":{"abstract_excerpt":"One of the main motivations for training high quality image generative models is their potential use as tools for image manipulation. Recently, generative adversarial networks (GANs) have been able to generate images of remarkable quality. Unfortunately, adversarially-trained unconditional generator networks have not been successful as image priors. One of the main requirements for a network to act as a generative image prior, is being able to generate every possible image from the target distribution. Adversarial learning often experiences mode-collapse, which manifests in generators that can","authors_text":"Aviv Gabbay, Yedid Hoshen","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-05T17:58:28Z","title":"Style Generator Inversion for Image Enhancement and Animation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11880","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:e62a368af2da3510f81f91c1406ac83aa3c711ca3e152d76a5488310e95d1a8b","target":"record","created_at":"2026-05-17T23:42:01Z","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":"d3e4d00dc54f25ee1c44fb716cf95fccaeaf1af0b1de98e981d4e11cfe7e99be","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-05T17:58:28Z","title_canon_sha256":"4a293ee61a0967579e9159cbe77507fa582cabb54b22748d35a1cafdc41d50ac"},"schema_version":"1.0","source":{"id":"1906.11880","kind":"arxiv","version":1}},"canonical_sha256":"078296692b30d9f02070ca318d23756ee0d61eb29b00db65347b46f193cbd7b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"078296692b30d9f02070ca318d23756ee0d61eb29b00db65347b46f193cbd7b1","first_computed_at":"2026-05-17T23:42:01.767361Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:01.767361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s0u703JFeqCO/3I5BjPpdrMA1lYliyP9v3HO7poFUNA/mOAPlObzGA9fAo7EFxT1s+wkIBNSgV5GrUDUnkehCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:01.767993Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.11880","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e62a368af2da3510f81f91c1406ac83aa3c711ca3e152d76a5488310e95d1a8b","sha256:20f236e0d7e3bdf310d02450b65c5bb4f8bc5dbf6aa710a4455bd26b4c69ef6a"],"state_sha256":"244078b5a62660a1c84a4b42df6221649131e346baf6556b5e1393f7b693d41f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mjSwPcIyCXpY1VzzJFcG6OSwT8WuzuoT/1vPf9KSI1118v0oseHaBuDnAgecw/IPKTJ+uV7aijc+9nFRkxHwDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T23:34:39.350124Z","bundle_sha256":"4e77afbab94f9771734d61b7f0c28a4deab9fb1ec18ecb4870e29a2a400d7355"}}