{"id":"W2971644068","doi":"","title":"GAN Data Augmentation Through Active Learning Inspired Sample Acquisition.","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Sample (material); 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":[0.0006133459,0.0007182847,0.0005539797,0.0003802461,0.000139139,0.0005544389,0.001071259,0.0006347506,0.002908346],"category_scores_gemma":[0.002481218,0.0003125738,0.0004240529,0.0005651141,0.0004491979,0.001094986,0.0008532042,0.001489136,0.001349118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002317359,"about_ca_system_score_gemma":0.000436594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008853235,"about_ca_topic_score_gemma":0.002005654,"domain_scores_codex":[0.9996958,0.0000680473,0.0000117736,0.00007822029,0.0001184805,0.00002777278],"domain_scores_gemma":[0.9992554,0.000289213,0.00004953767,0.0001823197,0.0001938842,0.0000296524],"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":[0.0005789052,0.0002685551,0.001694691,0.0002640801,0.0001340454,0.0001857919,0.0001603383,0.1901023,0.05522882,0.01891498,0.02062401,0.7118434],"study_design_scores_gemma":[0.000008622883,0.0000591583,0.0002744647,0.00001173826,0.00001029925,0.00008378329,0.00001101065,0.9767799,0.01522572,0.004074904,0.003451735,0.000008629908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01071879,0.0003004343,0.9848791,0.0001377754,0.000127669,0.00004707427,0.0002480684,0.001547512,0.001993612],"genre_scores_gemma":[0.4500871,0.0004522834,0.5372027,0.000407172,0.0001648855,0.0002328879,0.002204166,0.0003911075,0.008857695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002908346,"threshold_uncertainty_score":0.009729385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04842322416993738,"score_gpt":0.3216162387348098,"score_spread":0.2731930145648724,"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."}}