{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037208,0.0002055063,0.0002405566,0.0001531196,0.0005430548,0.0003724487,0.0002494173,0.0001063882,0.00001121093],"category_scores_gemma":[0.00001919046,0.0001948102,0.00009386187,0.0007753773,0.00003882434,0.0003065036,0.000002643302,0.0007665711,0.00001830238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002728492,"about_ca_system_score_gemma":0.0000283497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002555295,"about_ca_topic_score_gemma":0.00000332591,"domain_scores_codex":[0.9981534,0.0004549076,0.0003178455,0.0004631383,0.0003076937,0.0003029451],"domain_scores_gemma":[0.9990161,0.0003506892,0.0001562489,0.0001732575,0.000059832,0.0002438929],"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":[0.00002914824,0.00001777011,0.00002987117,0.00001797486,0.00001401518,0.000009325723,0.000719556,0.9681611,0.00007575502,0.0003869032,0.00003869828,0.03049986],"study_design_scores_gemma":[0.0005523051,0.0003003002,0.00006811336,0.00002700582,0.00001085184,0.000009970065,0.0003588031,0.9957945,0.00001991831,0.000005167107,0.00264117,0.0002118923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003935283,0.0002403183,0.9932182,0.001159127,0.000588871,0.0002202724,0.000001100698,0.0004252631,0.0002114928],"genre_scores_gemma":[0.9933046,0.00002087998,0.005196065,0.001043252,0.0001068895,0.00002787724,0.000002590565,0.00002239451,0.0002755038],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9893693,"threshold_uncertainty_score":0.7944129,"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."}}