{"id":"W4386075939","doi":"10.1109/cvpr52729.2023.01542","title":"Class Adaptive Network Calibration","year":2023,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Smoothing; Machine learning; Artificial intelligence; Class (philosophy); Code (set theory); Scalability; Artificial neural network; Calibration; Contextual image classification; Data mining; Image (mathematics); Mathematics; Computer vision","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002235245,0.0000439222,0.0000422819,0.00004005617,0.00009285031,0.00008949667,0.0002757209,0.00002606577,0.00002164412],"category_scores_gemma":[0.00002624704,0.00003704979,0.00001566025,0.0006156107,0.000008488923,0.0003253338,0.0001053795,0.00006376428,0.0006014838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008295671,"about_ca_system_score_gemma":0.00001994947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000244617,"about_ca_topic_score_gemma":0.00001330604,"domain_scores_codex":[0.9994318,0.00005506081,0.00008029343,0.0001829667,0.0001149006,0.0001349171],"domain_scores_gemma":[0.9995532,0.00006709908,0.00002963366,0.0002972147,0.00001746469,0.00003537521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001371978,0.0000044625,0.001155396,0.000001129279,0.000002793121,0.000001707842,0.00007621939,0.005316192,0.0000640841,0.8627365,0.0922609,0.03837923],"study_design_scores_gemma":[0.0000441778,0.00001943816,0.01131873,0.000002047132,6.236174e-7,0.000001142138,0.00001121045,0.9388422,0.00002594546,0.005447073,0.04423476,0.00005260135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005797849,0.000007678179,0.967414,0.004649409,0.0002144423,0.00004688665,5.947953e-7,0.0009071855,0.02617997],"genre_scores_gemma":[0.9418456,0.00001329407,0.04854,0.0007974611,0.0002434919,0.00001670383,0.00007054558,0.000006899232,0.008466034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9412658,"threshold_uncertainty_score":0.7731058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03016431155759341,"score_gpt":0.2619918875436199,"score_spread":0.2318275759860264,"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."}}