{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000546177,0.0003127932,0.0002967758,0.0001066412,0.0003670404,0.0003676465,0.001269363,0.0001567985,0.000397439],"category_scores_gemma":[0.00002553829,0.0002601064,0.0001492582,0.0004336222,0.00005157445,0.0002941462,0.002599807,0.001160414,0.000008355533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001492943,"about_ca_system_score_gemma":0.0000521558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001800174,"about_ca_topic_score_gemma":0.000126465,"domain_scores_codex":[0.9972867,0.0002553526,0.0003889881,0.00108105,0.0006009383,0.0003869237],"domain_scores_gemma":[0.9983783,0.00005533081,0.0002440884,0.001089598,0.00009883161,0.0001338888],"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":[0.00002045002,0.0001041439,0.009064159,0.0000519576,0.00004359694,0.00001296784,0.0006594364,0.9555393,0.000009482663,0.0008834132,0.00009614972,0.03351496],"study_design_scores_gemma":[0.0001818287,0.0001989205,0.006165419,0.000008663038,0.00001215809,0.0000185934,0.0002294469,0.9922286,0.00002871337,0.0003151862,0.0002948053,0.0003176236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06560802,0.0000790425,0.9262657,0.0005202876,0.002010748,0.0005480566,0.000002715676,0.0009323054,0.004033131],"genre_scores_gemma":[0.9927656,0.000006709033,0.005928186,0.0003075537,0.0001080783,0.0001276798,0.00002319414,0.00001912957,0.0007138556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9271576,"threshold_uncertainty_score":0.9999851,"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."}}