{"id":"W3202932137","doi":"10.48550/arxiv.2104.02424","title":"Teacher-Student Adversarial Depth Hallucination to Improve Face Recognition","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminator; Hallucinating; Computer science; RGB color model; Artificial intelligence; Face (sociological concept); Generator (circuit theory); Depth map; Pattern recognition (psychology); Computer vision; Facial recognition system; Image (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":[0.0007447582,0.001253139,0.0006911222,0.0003223925,0.0002056998,0.0005155411,0.001379183,0.0007192369,0.004125248],"category_scores_gemma":[0.002767212,0.0003444637,0.0008022747,0.0002781442,0.000626799,0.001063578,0.001797289,0.00187453,0.001397133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000504859,"about_ca_system_score_gemma":0.0005156216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001923456,"about_ca_topic_score_gemma":0.003382405,"domain_scores_codex":[0.9996108,0.00009426759,0.00001154021,0.0001119943,0.0001170169,0.00005428806],"domain_scores_gemma":[0.9994349,0.0002355335,0.00003807532,0.0001635184,0.00009191993,0.00003593538],"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":[0.0003763924,0.0001705368,0.001784654,0.0001647239,0.0001447456,0.0001653205,0.0001145772,0.6524777,0.02920799,0.008920702,0.01224593,0.2942267],"study_design_scores_gemma":[0.00001068014,0.00005393133,0.0001734405,0.000008145807,0.000009186912,0.00007955331,0.000008524766,0.9877409,0.007069541,0.003594675,0.001243521,0.000007921176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02252635,0.0005134054,0.9685412,0.000334481,0.0001209161,0.00006781978,0.0002590097,0.004075265,0.003561584],"genre_scores_gemma":[0.6522052,0.0004774081,0.3343126,0.0009270375,0.0001327458,0.0001791989,0.001337886,0.0007108368,0.009717189],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004125248,"threshold_uncertainty_score":0.01380032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0526758888711047,"score_gpt":0.2001021225386933,"score_spread":0.1474262336675886,"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."}}