{"id":"W2765872781","doi":"10.1109/avss.2017.8078553","title":"Using deep autoencoders to learn robust domain-invariant representations for still-to-video face recognition","year":2017,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Autoencoder; Computer vision; Facial recognition system; Face (sociological concept); Pattern recognition (psychology); Facial expression; Video quality; Matching (statistics); Deep learning; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003728987,0.0001460087,0.0001907319,0.0002436075,0.0008238333,0.0009027619,0.00075191,0.00005817975,0.0001253846],"category_scores_gemma":[0.0004914395,0.0001407648,0.000147029,0.000294412,0.00003344137,0.0007835802,0.0002585523,0.00007615062,0.0003617166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006543547,"about_ca_system_score_gemma":0.00006549971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003593266,"about_ca_topic_score_gemma":0.0005019193,"domain_scores_codex":[0.9985331,0.00005621317,0.0002620412,0.0005710315,0.0002391017,0.0003385],"domain_scores_gemma":[0.9983966,0.000114936,0.0001321915,0.0008158676,0.0002634033,0.0002769689],"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":[0.00008221697,0.0004147117,0.0007758354,0.00008438322,0.0004147528,0.0000441281,0.009380803,0.5590592,0.01434023,0.01782463,0.01420818,0.3833709],"study_design_scores_gemma":[0.0005390354,0.00006805194,0.000521585,0.00004908875,0.00004456984,0.000011404,0.001028317,0.9863495,0.002272506,0.006070252,0.00267532,0.0003704242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006026709,0.000003996611,0.9783505,0.01080228,0.0002423718,0.0004738357,0.00001590496,0.0001525068,0.003931902],"genre_scores_gemma":[0.09282281,0.00000480395,0.9038107,0.001649725,0.00007452254,0.00007009831,0.00001434781,0.00001428299,0.001538714],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4272903,"threshold_uncertainty_score":0.8705354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1184309926371423,"score_gpt":0.3402313567947307,"score_spread":0.2218003641575884,"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."}}