{"id":"W3128974447","doi":"10.21203/rs.3.rs-93060/v1","title":"Enhance Image Classification Performance Via Unsupervised Pre-trained Transformers Language Models","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tsinghua University; Canadian Institute for Advanced Research","keywords":"Computer science; Transformer; Artificial intelligence; Pattern recognition (psychology); Contextual image classification; Unsupervised learning; Machine learning; Natural language processing; Image (mathematics); Engineering; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006871262,0.001253015,0.000442574,0.0005711834,0.0002332999,0.0007490056,0.001599384,0.0006050045,0.00247984],"category_scores_gemma":[0.00227787,0.0003177741,0.0005621912,0.0004642011,0.0004285919,0.002402541,0.000886121,0.001595702,0.001793128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006104486,"about_ca_system_score_gemma":0.0008725147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004077497,"about_ca_topic_score_gemma":0.007819484,"domain_scores_codex":[0.999759,0.00005358539,0.00001401226,0.0000805198,0.00005226203,0.00004050002],"domain_scores_gemma":[0.9993656,0.0002370395,0.00005836356,0.0001241995,0.0001775064,0.0000372953],"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":[0.0004664508,0.0005894137,0.003124678,0.0001884798,0.0001250445,0.0002300466,0.0001656238,0.2400432,0.08074445,0.008938401,0.0138832,0.6515011],"study_design_scores_gemma":[0.00001712624,0.00009347102,0.0002931525,0.000007254625,0.0000229471,0.00004107893,0.00002113786,0.9776362,0.01817308,0.002839773,0.0008455451,0.000009269069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1961307,0.0007428899,0.7736756,0.0009129607,0.0002745831,0.0001445446,0.0005775202,0.01662526,0.01091594],"genre_scores_gemma":[0.8833926,0.000348392,0.1058789,0.0003860182,0.00007192197,0.000109611,0.001341942,0.000366752,0.008103926],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004077497,"threshold_uncertainty_score":0.008295953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03019822765891393,"score_gpt":0.2898025129843697,"score_spread":0.2596042853254557,"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."}}