{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZG7ELFIZBFEHI7L4SRONQRO7NO","short_pith_number":"pith:ZG7ELFIZ","schema_version":"1.0","canonical_sha256":"c9be4595190948747d7c945cd845df6b84ba280b430afa40707c70ba831baa85","source":{"kind":"arxiv","id":"2302.10698","version":1},"attestation_state":"computed","paper":{"title":"Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Lin Zhang, Orcun Goksel, Tiziano Portenier","submitted_at":"2023-02-21T14:36:18Z","abstract_excerpt":"Photorealistic image generation from simulated label maps are necessitated in several contexts, such as for medical training in virtual reality. With conventional deep learning methods, this task requires images that are paired with semantic annotations, which typically are unavailable. We introduce a contrastive learning framework for generating photorealistic images from simulated label maps, by learning from unpaired sets of both. Due to potentially large scene differences between real images and label maps, existing unpaired image translation methods lead to artifacts of scene modification"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2302.10698","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-02-21T14:36:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1b212fa55364d27a4c0b10d7ea2bcd0dd5210461096f35550991872205b20049","abstract_canon_sha256":"c3627a50490c915d736be9c08b0c7380248c6756b99f2ad03546eb26aec0f4e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:44:20.874494Z","signature_b64":"n0eVDmpAFZ5HjvIxRAGXnQqWa1rkWQ+5g8pa9YixLsZSwHOjSJwCOxZJTErKothDIFDWimeoT1xgPmnR5q3JDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9be4595190948747d7c945cd845df6b84ba280b430afa40707c70ba831baa85","last_reissued_at":"2026-07-05T05:44:20.874079Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:44:20.874079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Lin Zhang, Orcun Goksel, Tiziano Portenier","submitted_at":"2023-02-21T14:36:18Z","abstract_excerpt":"Photorealistic image generation from simulated label maps are necessitated in several contexts, such as for medical training in virtual reality. With conventional deep learning methods, this task requires images that are paired with semantic annotations, which typically are unavailable. We introduce a contrastive learning framework for generating photorealistic images from simulated label maps, by learning from unpaired sets of both. Due to potentially large scene differences between real images and label maps, existing unpaired image translation methods lead to artifacts of scene modification"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.10698","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/2302.10698/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2302.10698","created_at":"2026-07-05T05:44:20.874136+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.10698v1","created_at":"2026-07-05T05:44:20.874136+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.10698","created_at":"2026-07-05T05:44:20.874136+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZG7ELFIZBFEH","created_at":"2026-07-05T05:44:20.874136+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZG7ELFIZBFEHI7L4","created_at":"2026-07-05T05:44:20.874136+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZG7ELFIZ","created_at":"2026-07-05T05:44:20.874136+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZG7ELFIZBFEHI7L4SRONQRO7NO","json":"https://pith.science/pith/ZG7ELFIZBFEHI7L4SRONQRO7NO.json","graph_json":"https://pith.science/api/pith-number/ZG7ELFIZBFEHI7L4SRONQRO7NO/graph.json","events_json":"https://pith.science/api/pith-number/ZG7ELFIZBFEHI7L4SRONQRO7NO/events.json","paper":"https://pith.science/paper/ZG7ELFIZ"},"agent_actions":{"view_html":"https://pith.science/pith/ZG7ELFIZBFEHI7L4SRONQRO7NO","download_json":"https://pith.science/pith/ZG7ELFIZBFEHI7L4SRONQRO7NO.json","view_paper":"https://pith.science/paper/ZG7ELFIZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.10698&json=true","fetch_graph":"https://pith.science/api/pith-number/ZG7ELFIZBFEHI7L4SRONQRO7NO/graph.json","fetch_events":"https://pith.science/api/pith-number/ZG7ELFIZBFEHI7L4SRONQRO7NO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZG7ELFIZBFEHI7L4SRONQRO7NO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZG7ELFIZBFEHI7L4SRONQRO7NO/action/storage_attestation","attest_author":"https://pith.science/pith/ZG7ELFIZBFEHI7L4SRONQRO7NO/action/author_attestation","sign_citation":"https://pith.science/pith/ZG7ELFIZBFEHI7L4SRONQRO7NO/action/citation_signature","submit_replication":"https://pith.science/pith/ZG7ELFIZBFEHI7L4SRONQRO7NO/action/replication_record"}},"created_at":"2026-07-05T05:44:20.874136+00:00","updated_at":"2026-07-05T05:44:20.874136+00:00"}