{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:RNWAZJHV5QH6ECKPJHL3TFFG25","short_pith_number":"pith:RNWAZJHV","schema_version":"1.0","canonical_sha256":"8b6c0ca4f5ec0fe2094f49d7b994a6d74606b589621e4af313f3f2d4199dec5c","source":{"kind":"arxiv","id":"1811.11979","version":1},"attestation_state":"computed","paper":{"title":"Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fariborz Taherkhani, Hadi Kazemi, Nasser M. Nasrabadi, Seyed Mehdi Iranmanesh, Sobhan Soleymani","submitted_at":"2018-11-29T06:25:58Z","abstract_excerpt":"Unsupervised image-to-image translation is a class of computer vision problems which aims at modeling conditional distribution of images in the target domain, given a set of unpaired images in the source and target domains. An image in the source domain might have multiple representations in the target domain. Therefore, ambiguity in modeling of the conditional distribution arises, specially when the images in the source and target domains come from different modalities. Current approaches mostly rely on simplifying assumptions to map both domains into a shared-latent space. Consequently, they"},"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":"1811.11979","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-29T06:25:58Z","cross_cats_sorted":[],"title_canon_sha256":"feb2051e7a0b5ec2d6dd0e1cf68f1162cd9ebc909355dbb3a9854789c6096b0f","abstract_canon_sha256":"56a268ede1cfd090cb202f42436461b5a3288dc80e29d5b581b7c437ba94bda0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:34.854611Z","signature_b64":"pGvnggnKNMT9+xSkmHQwjdXnbSTOupWTqMj2GoejmuitwZck3JI4WXALvNUDFgWkS/aPkOXgOnq5epSTirDNAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b6c0ca4f5ec0fe2094f49d7b994a6d74606b589621e4af313f3f2d4199dec5c","last_reissued_at":"2026-05-17T23:59:34.854087Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:34.854087Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fariborz Taherkhani, Hadi Kazemi, Nasser M. Nasrabadi, Seyed Mehdi Iranmanesh, Sobhan Soleymani","submitted_at":"2018-11-29T06:25:58Z","abstract_excerpt":"Unsupervised image-to-image translation is a class of computer vision problems which aims at modeling conditional distribution of images in the target domain, given a set of unpaired images in the source and target domains. An image in the source domain might have multiple representations in the target domain. Therefore, ambiguity in modeling of the conditional distribution arises, specially when the images in the source and target domains come from different modalities. Current approaches mostly rely on simplifying assumptions to map both domains into a shared-latent space. Consequently, they"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11979","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1811.11979","created_at":"2026-05-17T23:59:34.854167+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.11979v1","created_at":"2026-05-17T23:59:34.854167+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.11979","created_at":"2026-05-17T23:59:34.854167+00:00"},{"alias_kind":"pith_short_12","alias_value":"RNWAZJHV5QH6","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"RNWAZJHV5QH6ECKP","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"RNWAZJHV","created_at":"2026-05-18T12:32:50.500415+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/RNWAZJHV5QH6ECKPJHL3TFFG25","json":"https://pith.science/pith/RNWAZJHV5QH6ECKPJHL3TFFG25.json","graph_json":"https://pith.science/api/pith-number/RNWAZJHV5QH6ECKPJHL3TFFG25/graph.json","events_json":"https://pith.science/api/pith-number/RNWAZJHV5QH6ECKPJHL3TFFG25/events.json","paper":"https://pith.science/paper/RNWAZJHV"},"agent_actions":{"view_html":"https://pith.science/pith/RNWAZJHV5QH6ECKPJHL3TFFG25","download_json":"https://pith.science/pith/RNWAZJHV5QH6ECKPJHL3TFFG25.json","view_paper":"https://pith.science/paper/RNWAZJHV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.11979&json=true","fetch_graph":"https://pith.science/api/pith-number/RNWAZJHV5QH6ECKPJHL3TFFG25/graph.json","fetch_events":"https://pith.science/api/pith-number/RNWAZJHV5QH6ECKPJHL3TFFG25/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RNWAZJHV5QH6ECKPJHL3TFFG25/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RNWAZJHV5QH6ECKPJHL3TFFG25/action/storage_attestation","attest_author":"https://pith.science/pith/RNWAZJHV5QH6ECKPJHL3TFFG25/action/author_attestation","sign_citation":"https://pith.science/pith/RNWAZJHV5QH6ECKPJHL3TFFG25/action/citation_signature","submit_replication":"https://pith.science/pith/RNWAZJHV5QH6ECKPJHL3TFFG25/action/replication_record"}},"created_at":"2026-05-17T23:59:34.854167+00:00","updated_at":"2026-05-17T23:59:34.854167+00:00"}