{"id":"W4402753812","doi":"10.1109/cvpr52733.2024.01268","title":"Posterior Distillation Sampling","year":2024,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Samsung; Neurosciences Research Foundation","keywords":"Sampling (signal processing); Computer science; Distillation; Chromatography; Computer vision; Chemistry","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":[0.00008165263,0.00003949706,0.00002965773,0.00004887036,0.00002310683,0.0002910609,0.0001783104,0.00001248315,0.00004593301],"category_scores_gemma":[0.000006663963,0.00003247326,0.00001683696,0.0001465487,0.000005945803,0.0004537879,0.00009662137,0.0000302127,0.000150319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002064788,"about_ca_system_score_gemma":0.00001173024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004570739,"about_ca_topic_score_gemma":8.312509e-7,"domain_scores_codex":[0.9996161,0.000005687395,0.00006735844,0.0001491078,0.00008234855,0.00007935659],"domain_scores_gemma":[0.9997796,0.00002262742,0.000005946065,0.0001643038,0.00001351292,0.00001399739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[4.813388e-7,0.000005894836,0.00004607656,0.00002395234,0.000004841372,0.00001032362,0.0002479027,0.000003827437,0.0393259,0.2111503,0.00282729,0.7463533],"study_design_scores_gemma":[0.00008132924,0.00012282,0.002675824,0.0001848869,0.000006313199,0.00005710093,0.00001200155,0.5382922,0.27941,0.03399108,0.1446889,0.0004775586],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002802146,0.00009268652,0.9825408,0.0007384098,0.0002696774,0.00005470117,2.243429e-7,0.00148827,0.01201312],"genre_scores_gemma":[0.7212411,0.000002765755,0.2773619,0.0001371009,0.00003495408,0.000004753171,5.601165e-7,0.000003095745,0.0012137],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7458757,"threshold_uncertainty_score":0.2806707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02328596196149229,"score_gpt":0.3042459701831524,"score_spread":0.2809600082216601,"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."}}