{"id":"W4385327582","doi":"10.48550/arxiv.2307.13755","title":"Training-based Model Refinement and Representation Disagreement for Semi-Supervised Object Detection","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; fRI Research","keywords":"Overfitting; Computer science; Pascal (unit); Artificial intelligence; Margin (machine learning); Machine learning; Representation (politics); Object (grammar); Generalization; Pattern recognition (psychology); Artificial neural network; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001800266,0.0002512911,0.0002349127,0.0001968006,0.0002596127,0.00007834688,0.0006428415,0.000142749,0.000001794828],"category_scores_gemma":[0.00003381538,0.0003027992,0.0001358905,0.0005190867,0.00005885089,0.0002205917,0.0006627791,0.0002309874,0.000007025129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002044898,"about_ca_system_score_gemma":0.0001038279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004251244,"about_ca_topic_score_gemma":0.0002227514,"domain_scores_codex":[0.9980823,0.00005583156,0.0002219981,0.001240882,0.0001021193,0.0002968373],"domain_scores_gemma":[0.9983848,0.0002086986,0.0002176311,0.000949726,0.0001192747,0.0001198434],"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":[0.00003087756,0.00002980426,0.00008893623,0.0000479945,0.00002792252,0.000005645365,0.00019058,0.9836808,0.0003494378,0.009899001,0.00008494066,0.005563988],"study_design_scores_gemma":[0.0005498706,0.0000548328,0.0003061512,0.00003872937,0.00004957943,5.441231e-7,0.00006369523,0.9345579,0.0005831885,0.0634113,0.0001106627,0.0002735365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04562645,0.00001407237,0.9521966,0.0004039043,0.0001994834,0.001011017,0.00003061985,0.0004214966,0.00009636716],"genre_scores_gemma":[0.9817389,0.00007392164,0.01723343,0.0001272351,0.00005597885,0.0000592392,0.00006456333,0.00002618291,0.0006205713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9361124,"threshold_uncertainty_score":0.9999424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2137014394924574,"score_gpt":0.2541779916524084,"score_spread":0.04047655215995097,"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."}}