{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008305536,0.0001283266,0.0001271579,0.00002863404,0.0001964219,0.00009632349,0.001727602,0.00002646706,0.00007228712],"category_scores_gemma":[0.000009952984,0.0001020153,0.0000144667,0.0004939124,0.00005424102,0.0003372954,0.001483469,0.0001187907,0.00009638789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002075408,"about_ca_system_score_gemma":0.00007796423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006467312,"about_ca_topic_score_gemma":0.00007665313,"domain_scores_codex":[0.9985371,0.00002146833,0.0001683473,0.000745959,0.000260744,0.0002664318],"domain_scores_gemma":[0.9967698,0.00006800242,0.0000572065,0.002933793,0.00006252459,0.000108652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003773471,0.002029527,0.005387651,0.00008189338,0.000224122,0.0009095971,0.0004462169,0.01675069,0.07608505,0.6320718,0.2286894,0.03728637],"study_design_scores_gemma":[0.002769314,0.0003345434,0.01904958,0.00006861505,0.00007343585,0.0004138284,0.000124068,0.4051562,0.08417379,0.001434542,0.4845622,0.001839904],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07429525,0.0001055817,0.9206741,0.001128003,0.00008961348,0.000265408,0.0002128397,0.0003942382,0.002834935],"genre_scores_gemma":[0.7866068,0.00000594238,0.2095152,0.0008828847,0.00008186787,0.00002593702,0.001895829,0.00001750353,0.0009680408],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7123116,"threshold_uncertainty_score":0.4160062,"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."}}