{"id":"W3003782762","doi":"10.1109/iscc47284.2019.8969638","title":"Data Augmentation using CA Evolved GANs","year":2019,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Face (sociological concept); Artificial neural network; Deep learning; Domain (mathematical analysis); Field (mathematics); Key (lock); Architecture; Task (project management); Data mining; Pattern recognition (psychology)","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":[0.0001472386,0.00006210792,0.00007122751,0.00002722248,0.00005459521,0.0001229151,0.0006658657,0.00001892396,0.000357245],"category_scores_gemma":[0.00001009484,0.00005200482,0.00001633956,0.0001416915,0.000008654528,0.00105365,0.0003294535,0.00003115566,0.0002417537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001923826,"about_ca_system_score_gemma":0.00003627265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001459507,"about_ca_topic_score_gemma":0.00003143816,"domain_scores_codex":[0.9993306,0.00003892346,0.00009459478,0.0002870096,0.0001216226,0.0001272373],"domain_scores_gemma":[0.9991834,0.00003261336,0.00003419535,0.0006837014,0.0000337638,0.00003232564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002499557,0.0002204703,0.007056891,0.00003649111,0.0001980573,0.00001645773,0.001071773,0.1675913,0.6422048,0.01815387,0.0723134,0.09111145],"study_design_scores_gemma":[0.0001317287,0.00001234037,0.0005828617,0.000004274408,0.000003912407,0.000001188472,0.00003170971,0.9908128,0.005111392,0.0001141343,0.003114044,0.00007963202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01450458,0.00002365082,0.9797414,0.0002391055,0.0004408372,0.0001006322,0.000005187336,0.00003982996,0.004904793],"genre_scores_gemma":[0.7352101,0.000003341632,0.2635719,0.0002804212,0.00007562605,5.980204e-7,0.00001800474,0.000003883484,0.0008360932],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8232214,"threshold_uncertainty_score":0.3911583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06243347281961659,"score_gpt":0.2898838024539386,"score_spread":0.227450329634322,"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."}}