{"id":"W2604117447","doi":"10.1109/iccv.2017.403","title":"Temporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition","year":2017,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Landmark; Artificial intelligence; Probabilistic logic; Generative model; Face (sociological concept); Invariant (physics); Embedding; Generative grammar; Inference; Pattern recognition (psychology); Deep learning; Algorithm; Machine 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006626162,0.0006173786,0.0004979216,0.0006094615,0.0001737688,0.0004894967,0.001154964,0.0005545941,0.001710843],"category_scores_gemma":[0.001531577,0.0003412106,0.001013959,0.0004946273,0.0005999223,0.0007692387,0.0007876536,0.001157614,0.0007291694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006199302,"about_ca_system_score_gemma":0.0005499216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003110958,"about_ca_topic_score_gemma":0.004085566,"domain_scores_codex":[0.9995434,0.00007679376,0.00001538531,0.0001639545,0.0001515123,0.00004896349],"domain_scores_gemma":[0.9996172,0.0001286915,0.00006373172,0.0001068757,0.00006179777,0.00002162904],"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":[0.0001832575,0.00008525657,0.002190675,0.00009714699,0.00008669949,0.0002367771,0.000124395,0.4811401,0.03224698,0.01733288,0.004264201,0.4620116],"study_design_scores_gemma":[0.000002344364,0.00003017174,0.0006271083,0.000005832348,0.00001322775,0.000204055,0.000009171809,0.9852223,0.005857334,0.006330622,0.001688522,0.000009333701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01697845,0.0005610972,0.9803509,0.0001239095,0.00005354223,0.00003792688,0.0001597615,0.0006558718,0.001078575],"genre_scores_gemma":[0.7066345,0.001489385,0.2787772,0.0003674664,0.0001937236,0.0001615553,0.001044151,0.0004081704,0.01092395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003110958,"threshold_uncertainty_score":0.006185651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04963075832118821,"score_gpt":0.2902676788491917,"score_spread":0.2406369205280035,"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."}}