{"id":"W2787266142","doi":"10.1109/crv.2019.00025","title":"Generative Adversarial Networks Using Adaptive Convolution","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Upsampling; Convolution (computer science); Computer science; Margin (machine learning); Generator (circuit theory); Context (archaeology); Artificial intelligence; Feature (linguistics); Baseline (sea); Pattern recognition (psychology); Image (mathematics); Algorithm; Machine learning; Artificial neural network; Power (physics)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007220684,0.000983999,0.0007152355,0.0004251307,0.0002005627,0.0005687442,0.001089797,0.0008378788,0.002830751],"category_scores_gemma":[0.002118631,0.000485092,0.0006535237,0.0004480789,0.0008478412,0.0009838153,0.001173649,0.001820471,0.00080242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006860057,"about_ca_system_score_gemma":0.0003576352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001909971,"about_ca_topic_score_gemma":0.002667088,"domain_scores_codex":[0.9996458,0.0001242705,0.00001063547,0.0001043016,0.00007521424,0.00003980178],"domain_scores_gemma":[0.9993782,0.0003942202,0.00004534912,0.00009736195,0.00005751914,0.00002726629],"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":[0.0000595007,0.00002023196,0.0003273853,0.0000366125,0.00004323125,0.00007284438,0.00003461486,0.9333669,0.003225209,0.02688633,0.002621545,0.03330553],"study_design_scores_gemma":[0.000003787746,0.000006852921,0.00003392324,0.000002851494,0.0000028165,0.00001419367,0.000001442674,0.990371,0.0004744423,0.008687796,0.0003982254,0.000002692284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01155871,0.0003238223,0.9830881,0.000263457,0.00005114025,0.00003101168,0.0001117078,0.0009442411,0.003627895],"genre_scores_gemma":[0.7958955,0.000572786,0.1896452,0.0004988363,0.0001350692,0.0001969657,0.0006232864,0.000362675,0.01206975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002830751,"threshold_uncertainty_score":0.009469807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03829399933512447,"score_gpt":0.2517887654718297,"score_spread":0.2134947661367052,"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."}}