{"id":"W4213417700","doi":"10.31234/osf.io/2a7hy","title":"FaceMemNet: Predicting Face Memorability with Deep Neural Networks","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canada First Research Excellence Fund; Compute Canada","keywords":"Face (sociological concept); Computer science; Artificial intelligence; Image (mathematics); Artificial neural network; Object (grammar); Computer vision; Property (philosophy); Deep neural networks; Pattern recognition (psychology); Machine learning","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":[0.0007021309,0.001156113,0.0004628439,0.001016691,0.000227568,0.0005055433,0.00115457,0.001018078,0.002880849],"category_scores_gemma":[0.002336664,0.0003638754,0.0006768184,0.0003956495,0.0002409445,0.0009899173,0.0005930544,0.001097811,0.0006655269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009122707,"about_ca_system_score_gemma":0.0003686958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009911831,"about_ca_topic_score_gemma":0.01327639,"domain_scores_codex":[0.9998283,0.00003012198,0.000007294609,0.00006474525,0.00003131784,0.00003820469],"domain_scores_gemma":[0.9995692,0.0002310446,0.0000467951,0.00004846478,0.00007624478,0.00002810118],"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":[0.001592316,0.0009068305,0.03114044,0.0002966383,0.0005493831,0.0003629554,0.0001212998,0.2747741,0.02370157,0.002112715,0.02706332,0.6373786],"study_design_scores_gemma":[0.00001580283,0.0000703824,0.00329053,0.000008797888,0.00001985344,0.00004309383,0.000009590458,0.9912958,0.003785087,0.0009934619,0.0004607094,0.000006889655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7141047,0.00329284,0.26424,0.0007593322,0.0004829964,0.0002944242,0.003782132,0.008339645,0.004703857],"genre_scores_gemma":[0.9230101,0.0003760511,0.06747984,0.000224073,0.0001247159,0.0001426241,0.003626117,0.0001221827,0.00489444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009911831,"threshold_uncertainty_score":0.01970828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02244824481772556,"score_gpt":0.271753613930363,"score_spread":0.2493053691126374,"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."}}