{"id":"W4312102546","doi":"10.1109/tencon55691.2022.9977590","title":"Data Augmentation Methods for Low Resolution Facial Images","year":2022,"lang":"en","type":"article","venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Regularization (linguistics); Pattern recognition (psychology); Face (sociological concept); Set (abstract data type); Data set; Training set; Resolution (logic); Image resolution; Machine learning; Data mining; Artificial neural network","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":[0.0007638191,0.0008385793,0.0006236242,0.0008509897,0.0002819236,0.000656168,0.0008986793,0.0006547752,0.004412038],"category_scores_gemma":[0.002054977,0.0003604635,0.0009714179,0.0008034782,0.0004386126,0.0009900598,0.0008485957,0.001790964,0.001671462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003526372,"about_ca_system_score_gemma":0.0004521538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002093708,"about_ca_topic_score_gemma":0.004442713,"domain_scores_codex":[0.9995373,0.00007715395,0.00002294395,0.0001127425,0.000209383,0.00004044118],"domain_scores_gemma":[0.999411,0.0001935552,0.00004963174,0.000174587,0.0001515924,0.00001965856],"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":[0.000191898,0.0001457644,0.001087023,0.0004075579,0.0001221564,0.0001960669,0.0001353579,0.05360682,0.1276681,0.005488975,0.01198762,0.7989627],"study_design_scores_gemma":[0.00002341519,0.0001568403,0.003042507,0.00007320002,0.00006950646,0.0007076049,0.00007749526,0.8513042,0.110455,0.007261295,0.02678172,0.00004712129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0200605,0.001191097,0.9719724,0.0003182588,0.0001700384,0.00009811859,0.0005423196,0.002582352,0.003064953],"genre_scores_gemma":[0.1970622,0.001752729,0.7904848,0.0004400237,0.0001159108,0.000244417,0.00231942,0.0004301856,0.007150249],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004412038,"threshold_uncertainty_score":0.01475972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1143844673576007,"score_gpt":0.4027301345308763,"score_spread":0.2883456671732756,"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."}}