{"id":"W3039027093","doi":"10.1007/s00371-020-01893-7","title":"AestheticNet: deep convolutional neural network for person identification from visual aesthetic","year":2020,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Identification (biology); Artificial intelligence; Computer science; Benchmark (surveying); Rank (graph theory); Biometrics; Preference; Beauty; Pattern recognition (psychology); Computer vision; Aesthetics; Art; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002397051,0.001081233,0.0005617167,0.0008927818,0.0002936034,0.0004768824,0.0007895946,0.0007956775,0.006851843],"category_scores_gemma":[0.0005442449,0.0003252717,0.0005521523,0.0006634991,0.0002349321,0.0007077715,0.0007852213,0.0007942221,0.002259073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007796063,"about_ca_system_score_gemma":0.0005336936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00899299,"about_ca_topic_score_gemma":0.02140079,"domain_scores_codex":[0.9998549,0.00001415941,0.000003432246,0.00005541658,0.00003175219,0.00004028014],"domain_scores_gemma":[0.9998949,0.00001493038,0.00001245728,0.00002333477,0.00003563484,0.00001870799],"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":[0.0005940754,0.00036108,0.004660302,0.0003044763,0.0001838776,0.0003316725,0.00008812384,0.0287551,0.07821518,0.004897077,0.05804269,0.8235664],"study_design_scores_gemma":[0.00004728699,0.0002091358,0.01080431,0.00005249901,0.00008969918,0.0004204804,0.00005229114,0.9230734,0.03785071,0.01265982,0.01469628,0.00004417045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2052544,0.003335831,0.7255167,0.0008092607,0.0008442993,0.0003764126,0.007784187,0.0336772,0.02240169],"genre_scores_gemma":[0.7310768,0.0009843262,0.2168579,0.0007558249,0.0002139105,0.0001795838,0.01078602,0.0007951804,0.03835056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00899299,"threshold_uncertainty_score":0.02292174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03358938648763714,"score_gpt":0.2782218167610383,"score_spread":0.2446324302734011,"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."}}