{"id":"W3167788848","doi":"10.1109/cvpr46437.2021.01001","title":"DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":231,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Generator (circuit theory); Artificial intelligence; Code (set theory); Face (sociological concept); Pixel; Object (grammar); Pattern recognition (psychology); Factory (object-oriented programming); Computer vision; Power (physics); Set (abstract data type)","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":[0.002878403,0.002648114,0.001318284,0.001781164,0.0007889772,0.001890292,0.004339268,0.001888289,0.01222656],"category_scores_gemma":[0.009382697,0.001392753,0.001815683,0.001512288,0.001134542,0.003176294,0.004204519,0.003256903,0.01017444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001269087,"about_ca_system_score_gemma":0.001955943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004090086,"about_ca_topic_score_gemma":0.01240505,"domain_scores_codex":[0.9975104,0.0006951306,0.0001216533,0.0009129171,0.0005726316,0.0001871688],"domain_scores_gemma":[0.9948404,0.00140111,0.0002128565,0.002718837,0.0006805648,0.0001461027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001590916,0.0006083556,0.005401006,0.001281894,0.0005346412,0.0005341764,0.0004427072,0.07412215,0.03526362,0.02447415,0.3072573,0.5484891],"study_design_scores_gemma":[0.0004347592,0.0003986258,0.002780437,0.0002565717,0.0001044153,0.0007305524,0.0003197439,0.7255489,0.04674739,0.08962899,0.1328597,0.0001899858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0135295,0.0006336832,0.8627734,0.0006390632,0.0003692392,0.0007954499,0.02650453,0.08603691,0.008718248],"genre_scores_gemma":[0.08559543,0.0002751866,0.8018078,0.001011884,0.000104069,0.001557251,0.0982817,0.006066072,0.005300498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01222656,"threshold_uncertainty_score":0.0409019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05134722377062128,"score_gpt":0.3075878291716737,"score_spread":0.2562406054010524,"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."}}