{"id":"W2963444790","doi":"10.1109/iccv.2017.310","title":"DualGAN: Unsupervised Dual Learning for Image-to-Image Translation","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":2105,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image translation; Computer science; Translation (biology); Image (mathematics); Artificial intelligence; Dual (grammatical number); Task (project management); Domain (mathematical analysis); Pattern recognition (psychology); Labeled data; Generative grammar; Function (biology); Computer vision; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001127097,0.001326463,0.0009801754,0.0005606664,0.0003070854,0.0008223092,0.001593455,0.001116411,0.003620581],"category_scores_gemma":[0.002390318,0.0005181215,0.0007904262,0.0005059296,0.001095847,0.001368386,0.002211466,0.002550325,0.001521915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006994885,"about_ca_system_score_gemma":0.000708635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001134399,"about_ca_topic_score_gemma":0.0016857,"domain_scores_codex":[0.9994579,0.0001908091,0.00001706876,0.0001446843,0.0001301969,0.00005940585],"domain_scores_gemma":[0.999351,0.0002944907,0.00005621514,0.0001779735,0.00007950183,0.00004086756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003454404,0.0001809292,0.001088945,0.0002288811,0.0001656419,0.0001995356,0.000109692,0.6885701,0.01804736,0.06876479,0.01281008,0.2094886],"study_design_scores_gemma":[0.00001161256,0.0000230028,0.00006989889,0.000008407973,0.000005899341,0.00003874616,0.000004392145,0.9830373,0.002373703,0.0130153,0.001405632,0.000006164089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005469548,0.0002531788,0.9903487,0.000160596,0.00004630087,0.00003326496,0.00009975487,0.001051433,0.002537175],"genre_scores_gemma":[0.4927226,0.0006050239,0.4901761,0.000728279,0.0001605018,0.0003381674,0.001489204,0.0007778906,0.01300222],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003620581,"threshold_uncertainty_score":0.01211208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0425718938893782,"score_gpt":0.2904346791370451,"score_spread":0.2478627852476669,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}