{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:IRKS6MQRYISI4GBMMWP7IOCHUF","short_pith_number":"pith:IRKS6MQR","schema_version":"1.0","canonical_sha256":"44552f3211c2248e182c659ff43847a14160d43a1e4ad7cb36d499b9f2a19494","source":{"kind":"arxiv","id":"1703.10593","version":7},"attestation_state":"computed","paper":{"title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexei A. Efros, Jun-Yan Zhu, Phillip Isola, Taesung Park","submitted_at":"2017-03-30T17:44:17Z","abstract_excerpt":"Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain $X$ to a target domain $Y$ in the absence of paired examples. Our goal is to learn a mapping $G: X \\rightarrow Y$ such that the distribution of images from $G(X)$ is indistinguishable from the distribution $Y$ using an adversarial loss. Because this mappi"},"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":"1703.10593","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-30T17:44:17Z","cross_cats_sorted":[],"title_canon_sha256":"5a984fec85cf18c5dbd101eb5d8c5d105cb37757ff2790df07b59b440e0aaf05","abstract_canon_sha256":"17c653f45c35ed9fd843e4adfaf3035f80dbf3eeeb93289d7cb894f9ab72c487"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:28:57.516128Z","signature_b64":"Po8VHxZP21/gi856GbGX7k47uhozsKCnoRAp0WpLq6tf0o3B6TMzwAfcWWjCmC310IBn0bejKpxSRQeF1hf1Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44552f3211c2248e182c659ff43847a14160d43a1e4ad7cb36d499b9f2a19494","last_reissued_at":"2026-07-05T01:28:57.515605Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:28:57.515605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexei A. Efros, Jun-Yan Zhu, Phillip Isola, Taesung Park","submitted_at":"2017-03-30T17:44:17Z","abstract_excerpt":"Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain $X$ to a target domain $Y$ in the absence of paired examples. Our goal is to learn a mapping $G: X \\rightarrow Y$ such that the distribution of images from $G(X)$ is indistinguishable from the distribution $Y$ using an adversarial loss. Because this mappi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.10593","kind":"arxiv","version":7},"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/1703.10593/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":"1703.10593","created_at":"2026-07-05T01:28:57.515665+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.10593v7","created_at":"2026-07-05T01:28:57.515665+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.10593","created_at":"2026-07-05T01:28:57.515665+00:00"},{"alias_kind":"pith_short_12","alias_value":"IRKS6MQRYISI","created_at":"2026-07-05T01:28:57.515665+00:00"},{"alias_kind":"pith_short_16","alias_value":"IRKS6MQRYISI4GBM","created_at":"2026-07-05T01:28:57.515665+00:00"},{"alias_kind":"pith_short_8","alias_value":"IRKS6MQR","created_at":"2026-07-05T01:28:57.515665+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":18,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21588","citing_title":"Unsupervised Susceptibility Distortion Correction of EPI without Calibration Scans via Image Translation-Based Registration","ref_index":67,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01819","citing_title":"Hist2Style: Histogram-Guided Stylization with Bilateral Grids","ref_index":60,"is_internal_anchor":false},{"citing_arxiv_id":"1906.09691","citing_title":"Adversarial Computation of Optimal Transport Maps","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"1906.11080","citing_title":"AGAN: Towards Automated Design of Generative Adversarial Networks","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"1906.10907","citing_title":"Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"1907.01144","citing_title":"Disentangled Makeup Transfer with Generative Adversarial Network","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"1907.03426","citing_title":"Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"1907.07769","citing_title":"Hierarchical Sequence to Sequence Voice Conversion with Limited Data","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"1801.01401","citing_title":"Demystifying MMD GANs","ref_index":62,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03637","citing_title":"SAGE-GAN: Towards Realistic and Robust Segmentation of Spatially Ordered Nanoparticles via Attention-Guided GANs","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"1710.10196","citing_title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04769","citing_title":"Lightweight Cross-Spectral Face Recognition via Contrastive Alignment and Distillation","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18251","citing_title":"Style-Based Neural Architectures for Real-Time Weather Classification","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07860","citing_title":"On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13947","citing_title":"Heuristic Style Transfer for Real-Time, Efficient Weather Attribute Detection","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16086","citing_title":"Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21801","citing_title":"SyMTRS: Benchmark Multi-Task Synthetic Dataset for Depth, Domain Adaptation and Super-Resolution in Aerial Imagery","ref_index":51,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02471","citing_title":"Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IRKS6MQRYISI4GBMMWP7IOCHUF","json":"https://pith.science/pith/IRKS6MQRYISI4GBMMWP7IOCHUF.json","graph_json":"https://pith.science/api/pith-number/IRKS6MQRYISI4GBMMWP7IOCHUF/graph.json","events_json":"https://pith.science/api/pith-number/IRKS6MQRYISI4GBMMWP7IOCHUF/events.json","paper":"https://pith.science/paper/IRKS6MQR"},"agent_actions":{"view_html":"https://pith.science/pith/IRKS6MQRYISI4GBMMWP7IOCHUF","download_json":"https://pith.science/pith/IRKS6MQRYISI4GBMMWP7IOCHUF.json","view_paper":"https://pith.science/paper/IRKS6MQR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.10593&json=true","fetch_graph":"https://pith.science/api/pith-number/IRKS6MQRYISI4GBMMWP7IOCHUF/graph.json","fetch_events":"https://pith.science/api/pith-number/IRKS6MQRYISI4GBMMWP7IOCHUF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IRKS6MQRYISI4GBMMWP7IOCHUF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IRKS6MQRYISI4GBMMWP7IOCHUF/action/storage_attestation","attest_author":"https://pith.science/pith/IRKS6MQRYISI4GBMMWP7IOCHUF/action/author_attestation","sign_citation":"https://pith.science/pith/IRKS6MQRYISI4GBMMWP7IOCHUF/action/citation_signature","submit_replication":"https://pith.science/pith/IRKS6MQRYISI4GBMMWP7IOCHUF/action/replication_record"}},"created_at":"2026-07-05T01:28:57.515665+00:00","updated_at":"2026-07-05T01:28:57.515665+00:00"}