{"id":"W2999918589","doi":"10.1109/tnnls.2019.2957229","title":"LogDet Metric-Based Domain Adaptation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; Australian Research Council; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Metric (unit); Computer science; Domain adaptation; Curse of dimensionality; Domain (mathematical analysis); Norm (philosophy); Transformation (genetics); Adaptation (eye); Dimensionality reduction; Algorithm; Artificial intelligence; Machine learning; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001626441,0.001176473,0.001184514,0.001005262,0.0004300489,0.001132984,0.001533149,0.001324903,0.002547268],"category_scores_gemma":[0.006326703,0.0003498874,0.0008833665,0.001242711,0.001044556,0.002491842,0.002393956,0.00196069,0.001910942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007630385,"about_ca_system_score_gemma":0.001264165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001921593,"about_ca_topic_score_gemma":0.002344864,"domain_scores_codex":[0.9985899,0.000419428,0.00008232181,0.0004307455,0.000382391,0.00009508711],"domain_scores_gemma":[0.9986523,0.0004835906,0.0001056401,0.0003003244,0.0003887142,0.00006954052],"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":[0.0002696302,0.0002254547,0.001744927,0.0002617795,0.0001148012,0.0002005811,0.0001897069,0.3069724,0.02285244,0.03494892,0.01070403,0.6215154],"study_design_scores_gemma":[0.00001216447,0.00008502997,0.0005772247,0.00001531681,0.00001504748,0.0002286577,0.00003245742,0.9718076,0.006437906,0.01602992,0.004726612,0.00003214616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006121202,0.000337862,0.9909774,0.0001113598,0.00007442589,0.00004170926,0.00007438217,0.0008216975,0.001439927],"genre_scores_gemma":[0.4376879,0.0010686,0.5466413,0.0006780173,0.0002665356,0.0003621798,0.001575372,0.0008177675,0.01090227],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002547268,"threshold_uncertainty_score":0.008601546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02827943271169532,"score_gpt":0.2277200394930868,"score_spread":0.1994406067813914,"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."}}