{"id":"W3159134235","doi":"10.1109/icpr48806.2021.9412168","title":"A Distinct Discriminant Canonical Correlation Analysis Network based Deep Information Quality Representation for Image Classification","year":2021,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Canonical correlation; Discriminant; Computer science; Feature (linguistics); Face (sociological concept); Representation (politics); Contextual image classification; Feature extraction; Correlation; Class (philosophy); Facial recognition system; Image (mathematics); Data mining; 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":[0.0004708736,0.0001717114,0.0002717394,0.000149207,0.0001502609,0.0002109498,0.00008627345,0.0001296204,0.00005822575],"category_scores_gemma":[0.0007242325,0.000177026,0.0002124895,0.001151216,0.00004291451,0.0007996529,0.00001513599,0.0001215701,0.00003487481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002818412,"about_ca_system_score_gemma":0.00008001244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006123148,"about_ca_topic_score_gemma":0.0004967003,"domain_scores_codex":[0.9983073,0.0001350525,0.0007512925,0.0002680805,0.0002780173,0.0002601836],"domain_scores_gemma":[0.9982913,0.0003946122,0.0001922503,0.0005585563,0.0004804214,0.00008287523],"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":[0.0001392691,0.0001200165,0.02120497,0.0003457538,0.0005012836,0.000003824081,0.0005977976,0.8492641,0.0376343,0.007543094,0.005370753,0.07727486],"study_design_scores_gemma":[0.0002856084,0.000007005456,0.2710657,0.000008166016,0.0002638697,0.000001303249,0.0001589304,0.7253519,0.001688363,0.0001765153,0.0008314654,0.0001612424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01919937,0.00002187464,0.9748441,0.0004686537,0.0003181822,0.0003735354,0.00002767702,0.0003438577,0.00440272],"genre_scores_gemma":[0.9191115,0.000007344568,0.07576203,0.00006094478,0.00009878609,0.00004983249,0.004771316,0.00002170272,0.0001165013],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8999122,"threshold_uncertainty_score":0.721891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03984646608646367,"score_gpt":0.3087110716101136,"score_spread":0.2688646055236499,"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."}}