{"id":"W171902450","doi":"10.1007/978-3-642-33783-3_58","title":"Disentangling Factors of Variation for Facial Expression Recognition","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":220,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Discriminative model; Artificial intelligence; Pattern recognition (psychology); Facial expression; Variation (astronomy); Facial recognition system; Identity (music); Face (sociological concept); Convolutional neural network; Feature (linguistics); Representation (politics); Expression (computer science); Computer vision","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005186834,0.0003296394,0.0003761419,0.0005815602,0.000199821,0.0001464587,0.0009739739,0.0003225144,0.00003086142],"category_scores_gemma":[0.0001215119,0.0002852257,0.000152708,0.0002483586,0.000162206,0.0009779144,0.0003907334,0.0002879216,0.00001395234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001269501,"about_ca_system_score_gemma":0.0001522679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006989499,"about_ca_topic_score_gemma":0.000006707537,"domain_scores_codex":[0.9976549,0.00002767223,0.0004906573,0.0007814754,0.0006301335,0.0004151583],"domain_scores_gemma":[0.9981477,0.0004666345,0.0004507916,0.0005224824,0.0002918495,0.0001205537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001983665,0.00005787712,0.0001562127,0.00009931588,0.000007845604,0.000001793036,0.002060452,0.001542565,0.01173834,0.0007985661,0.00002020567,0.983497],"study_design_scores_gemma":[0.00109063,0.0004177864,0.0006142852,0.002329691,0.00004798507,0.00001290962,0.000002048303,0.3151087,0.3171873,0.3604559,0.001234301,0.001498485],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00212804,0.0001551743,0.994673,0.00007911447,0.00201796,0.0004997073,0.00003963106,0.00008088921,0.0003264573],"genre_scores_gemma":[0.5202145,0.00006207627,0.4785523,0.0002073628,0.0007138665,0.00003031144,0.0001120831,0.00003761624,0.00006986416],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9819985,"threshold_uncertainty_score":0.99996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03409717337005101,"score_gpt":0.2576889017436504,"score_spread":0.2235917283735994,"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."}}