{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000177847,0.0001894158,0.0001898683,0.0001110217,0.000176729,0.0002684855,0.0006770049,0.00008037078,0.0001474932],"category_scores_gemma":[0.00001619596,0.0001764157,0.00003057582,0.0001901543,0.00002766053,0.003331227,0.001003329,0.0002105861,0.00019606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003015561,"about_ca_system_score_gemma":0.00001517571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003991237,"about_ca_topic_score_gemma":0.000002705188,"domain_scores_codex":[0.9983101,0.0001762942,0.0002675276,0.0007770754,0.000265345,0.000203626],"domain_scores_gemma":[0.9985989,0.0002637487,0.0001934237,0.0007721707,0.0001000379,0.00007169636],"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":[0.00001611353,0.00004735694,0.0005250756,0.00002552125,0.000009301678,0.000003674848,0.0002932099,0.00003345764,0.0009933856,0.00004689148,0.001056522,0.9969495],"study_design_scores_gemma":[0.002348918,0.001108082,0.02153455,0.0007835432,0.00002189858,0.00007674305,0.0001081429,0.912795,0.01732642,0.02056012,0.02240387,0.000932731],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04045453,0.00003647376,0.9578285,0.000250406,0.0003880069,0.0003311076,0.0001031999,0.0004248416,0.0001829966],"genre_scores_gemma":[0.6897653,0.0001664956,0.3051864,0.001782968,0.0001534747,0.00001814605,0.002885331,0.00002086449,0.0000209838],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9960167,"threshold_uncertainty_score":0.7194023,"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."}}