{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005830186,0.0005423909,0.0004632189,0.0003594113,0.0002037881,0.0005478802,0.0007232474,0.0006545132,0.001511666],"category_scores_gemma":[0.002186767,0.000223583,0.0005491446,0.0002583811,0.0005906555,0.0004449918,0.000685413,0.0008583574,0.0002007659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006057064,"about_ca_system_score_gemma":0.0004803908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002274615,"about_ca_topic_score_gemma":0.002134204,"domain_scores_codex":[0.9998412,0.00004439985,0.000008348436,0.00003963938,0.00004029365,0.0000260749],"domain_scores_gemma":[0.9994494,0.0002662889,0.00004679123,0.00006488925,0.0001384753,0.00003411278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003746098,0.00002644466,0.0006824879,0.00001941975,0.0000230442,0.00007459084,0.0000449108,0.9667957,0.004731441,0.005295949,0.0005975791,0.02167091],"study_design_scores_gemma":[0.000002660495,0.000009904831,0.00005528445,0.000001933317,0.000002402113,0.00001111454,0.000003516774,0.9980353,0.0006246959,0.001038472,0.0002129872,0.000001781176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1368457,0.0002916315,0.8538741,0.0004257115,0.0001326233,0.00008009616,0.000104563,0.0007497545,0.007495819],"genre_scores_gemma":[0.8950134,0.0001171303,0.1011093,0.0001835483,0.00002043313,0.0001255525,0.000161506,0.00006295511,0.003206149],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002274615,"threshold_uncertainty_score":0.005057037,"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."}}