{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:VPNDCV42YPJF4NODCSBYARFVNB","short_pith_number":"pith:VPNDCV42","canonical_record":{"source":{"id":"2305.09726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-16T18:01:12Z","cross_cats_sorted":[],"title_canon_sha256":"a3291132f65f99e67da7c98ca9605cf6cd1e7b85d9af77e1a1cc75c40d6b0673","abstract_canon_sha256":"bae06c51e6262febcf9366094b3fad7c4c102d7dc7e57c97f984a83ba2f0d90a"},"schema_version":"1.0"},"canonical_sha256":"abda31579ac3d25e35c314838044b5686f00acfdec2f9ed7ee3b75bfd9fdc8d8","source":{"kind":"arxiv","id":"2305.09726","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.09726","created_at":"2026-07-05T06:10:53Z"},{"alias_kind":"arxiv_version","alias_value":"2305.09726v1","created_at":"2026-07-05T06:10:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.09726","created_at":"2026-07-05T06:10:53Z"},{"alias_kind":"pith_short_12","alias_value":"VPNDCV42YPJF","created_at":"2026-07-05T06:10:53Z"},{"alias_kind":"pith_short_16","alias_value":"VPNDCV42YPJF4NOD","created_at":"2026-07-05T06:10:53Z"},{"alias_kind":"pith_short_8","alias_value":"VPNDCV42","created_at":"2026-07-05T06:10:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:VPNDCV42YPJF4NODCSBYARFVNB","target":"record","payload":{"canonical_record":{"source":{"id":"2305.09726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-16T18:01:12Z","cross_cats_sorted":[],"title_canon_sha256":"a3291132f65f99e67da7c98ca9605cf6cd1e7b85d9af77e1a1cc75c40d6b0673","abstract_canon_sha256":"bae06c51e6262febcf9366094b3fad7c4c102d7dc7e57c97f984a83ba2f0d90a"},"schema_version":"1.0"},"canonical_sha256":"abda31579ac3d25e35c314838044b5686f00acfdec2f9ed7ee3b75bfd9fdc8d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:10:53.193673Z","signature_b64":"l3CDQcs7Xm/cNyedUgzpew62L9Mj5u2Ec0glm51ovFCwswRwez9uo4sd3KWpbrm7yB9q8hYn+jfcc9YAfa/BBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abda31579ac3d25e35c314838044b5686f00acfdec2f9ed7ee3b75bfd9fdc8d8","last_reissued_at":"2026-07-05T06:10:53.193179Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:10:53.193179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.09726","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":"2026-07-05T06:10:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hn5xNIfU86bDyb0Sc94o3Z0ESLsIxZLwg57poh8XQ4bPOS9v+kJxZCwwXUPsVsufvVmzQI7pCShHSIxleytIDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T11:23:29.692586Z"},"content_sha256":"dbd3a1c8a75da57fd0a076adc32c1b91d24eb435ba41368ce116af9050efbffe","schema_version":"1.0","event_id":"sha256:dbd3a1c8a75da57fd0a076adc32c1b91d24eb435ba41368ce116af9050efbffe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:VPNDCV42YPJF4NODCSBYARFVNB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Pragmatic Semantic Image Synthesis for Urban Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Yang, Diandian Guo, George Eskandar, Karim Guirguis","submitted_at":"2023-05-16T18:01:12Z","abstract_excerpt":"The need for large amounts of training and validation data is a huge concern in scaling AI algorithms for autonomous driving. Semantic Image Synthesis (SIS), or label-to-image translation, promises to address this issue by translating semantic layouts to images, providing a controllable generation of photorealistic data. However, they require a large amount of paired data, incurring extra costs. In this work, we present a new task: given a dataset with synthetic images and labels and a dataset with unlabeled real images, our goal is to learn a model that can generate images with the content of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.09726","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/2305.09726/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":"2026-07-05T06:10:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"noRk0hUiqIUgZFHXyoJBGvR3S6bARzI4ApKKkODf1X4phfOcqVbe/iC/p3XyUg6QjBm6qqadoRkFE1FSDuZqDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T11:23:29.692958Z"},"content_sha256":"329be5c483889a3f1fcb0b270aaac3107f29a07144537bf613ceec69ac5e1509","schema_version":"1.0","event_id":"sha256:329be5c483889a3f1fcb0b270aaac3107f29a07144537bf613ceec69ac5e1509"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VPNDCV42YPJF4NODCSBYARFVNB/bundle.json","state_url":"https://pith.science/pith/VPNDCV42YPJF4NODCSBYARFVNB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VPNDCV42YPJF4NODCSBYARFVNB/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":"2026-07-06T11:23:29Z","links":{"resolver":"https://pith.science/pith/VPNDCV42YPJF4NODCSBYARFVNB","bundle":"https://pith.science/pith/VPNDCV42YPJF4NODCSBYARFVNB/bundle.json","state":"https://pith.science/pith/VPNDCV42YPJF4NODCSBYARFVNB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VPNDCV42YPJF4NODCSBYARFVNB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:VPNDCV42YPJF4NODCSBYARFVNB","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":"bae06c51e6262febcf9366094b3fad7c4c102d7dc7e57c97f984a83ba2f0d90a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-16T18:01:12Z","title_canon_sha256":"a3291132f65f99e67da7c98ca9605cf6cd1e7b85d9af77e1a1cc75c40d6b0673"},"schema_version":"1.0","source":{"id":"2305.09726","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.09726","created_at":"2026-07-05T06:10:53Z"},{"alias_kind":"arxiv_version","alias_value":"2305.09726v1","created_at":"2026-07-05T06:10:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.09726","created_at":"2026-07-05T06:10:53Z"},{"alias_kind":"pith_short_12","alias_value":"VPNDCV42YPJF","created_at":"2026-07-05T06:10:53Z"},{"alias_kind":"pith_short_16","alias_value":"VPNDCV42YPJF4NOD","created_at":"2026-07-05T06:10:53Z"},{"alias_kind":"pith_short_8","alias_value":"VPNDCV42","created_at":"2026-07-05T06:10:53Z"}],"graph_snapshots":[{"event_id":"sha256:329be5c483889a3f1fcb0b270aaac3107f29a07144537bf613ceec69ac5e1509","target":"graph","created_at":"2026-07-05T06:10:53Z","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/2305.09726/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The need for large amounts of training and validation data is a huge concern in scaling AI algorithms for autonomous driving. Semantic Image Synthesis (SIS), or label-to-image translation, promises to address this issue by translating semantic layouts to images, providing a controllable generation of photorealistic data. However, they require a large amount of paired data, incurring extra costs. In this work, we present a new task: given a dataset with synthetic images and labels and a dataset with unlabeled real images, our goal is to learn a model that can generate images with the content of","authors_text":"Bin Yang, Diandian Guo, George Eskandar, Karim Guirguis","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-16T18:01:12Z","title":"Towards Pragmatic Semantic Image Synthesis for Urban Scenes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.09726","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:dbd3a1c8a75da57fd0a076adc32c1b91d24eb435ba41368ce116af9050efbffe","target":"record","created_at":"2026-07-05T06:10:53Z","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":"bae06c51e6262febcf9366094b3fad7c4c102d7dc7e57c97f984a83ba2f0d90a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-16T18:01:12Z","title_canon_sha256":"a3291132f65f99e67da7c98ca9605cf6cd1e7b85d9af77e1a1cc75c40d6b0673"},"schema_version":"1.0","source":{"id":"2305.09726","kind":"arxiv","version":1}},"canonical_sha256":"abda31579ac3d25e35c314838044b5686f00acfdec2f9ed7ee3b75bfd9fdc8d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"abda31579ac3d25e35c314838044b5686f00acfdec2f9ed7ee3b75bfd9fdc8d8","first_computed_at":"2026-07-05T06:10:53.193179Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:10:53.193179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"l3CDQcs7Xm/cNyedUgzpew62L9Mj5u2Ec0glm51ovFCwswRwez9uo4sd3KWpbrm7yB9q8hYn+jfcc9YAfa/BBw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:10:53.193673Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.09726","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dbd3a1c8a75da57fd0a076adc32c1b91d24eb435ba41368ce116af9050efbffe","sha256:329be5c483889a3f1fcb0b270aaac3107f29a07144537bf613ceec69ac5e1509"],"state_sha256":"94c138b85f8f08e44fc31dc0652364876d5f320c8bc0e623581dd4737cd79db3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bsd7zZpbBUr9ayhECUb5B/NuL6zLROHEQoVOBprY9vJcoVaqfLrX/+tmvT+eO/MW6c9j/CTxa0bpye3RQlwnCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T11:23:29.694932Z","bundle_sha256":"2f013cf33ccaa825cb999f78b0f67c6b1ebb83760c6fc942cd768b5fdcc2e910"}}