{"id":"W4389668005","doi":"10.1109/iros55552.2023.10342415","title":"What to Learn: Features, Image Transformations, or Both?","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Feature (linguistics); Transformation (genetics); Computer vision; Image (mathematics); Pattern recognition (psychology); Transfer of learning; Artificial neural network; Feature extraction; Matching (statistics); Robustness (evolution); Invariant (physics); Term (time); Robotics; Machine learning; Robot; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001326111,0.0009798239,0.0008117154,0.0004612575,0.0002655152,0.001084658,0.001082127,0.001635899,0.003418834],"category_scores_gemma":[0.007075976,0.0002795391,0.000449284,0.0005712229,0.001112762,0.005325743,0.0006892295,0.001582712,0.001549323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003498906,"about_ca_system_score_gemma":0.0006665406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002235091,"about_ca_topic_score_gemma":0.002098221,"domain_scores_codex":[0.9995444,0.0001056221,0.00002170054,0.0002089712,0.00006695255,0.00005234089],"domain_scores_gemma":[0.9982919,0.0009137957,0.0001812038,0.0002621179,0.0002454932,0.0001053709],"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":[0.000195941,0.0002745142,0.00855984,0.0004417434,0.00009699846,0.0001017743,0.0001048827,0.02493932,0.004587414,0.009280995,0.01359983,0.9378168],"study_design_scores_gemma":[0.0001264428,0.0006734665,0.01277854,0.0003859717,0.0001869706,0.0009651057,0.0007298678,0.7719311,0.01500314,0.1763466,0.02075086,0.0001218843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09359875,0.01088348,0.8647472,0.0178769,0.0006594411,0.0001367491,0.0008657855,0.002551307,0.008680413],"genre_scores_gemma":[0.8397184,0.004126035,0.1459393,0.002255087,0.0007023406,0.000144119,0.0009256288,0.0002002752,0.005988955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003418834,"threshold_uncertainty_score":0.01143712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268046564759954,"score_gpt":0.2390988775793706,"score_spread":0.226418411931771,"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."}}