{"id":"W2972137171","doi":"10.1007/978-3-030-33391-1_3","title":"Multi-layer Domain Adaptation for Deep Convolutional Networks","year":2019,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Domain adaptation; Domain (mathematical analysis); Generalization; Convolutional neural network; Artificial intelligence; Variance (accounting); Adaptation (eye); Layer (electronics); Sample (material); Deep learning; Test data; Machine learning; Class (philosophy); Pattern recognition (psychology); Data mining; 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":[0.0006762742,0.0007772449,0.0005216577,0.0003954515,0.0002430393,0.0005212322,0.001236481,0.0011448,0.003772479],"category_scores_gemma":[0.001893842,0.0005338612,0.0006954634,0.0006377973,0.0003403114,0.0009648564,0.00108521,0.002057006,0.001417547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007091715,"about_ca_system_score_gemma":0.000782543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007423937,"about_ca_topic_score_gemma":0.01109966,"domain_scores_codex":[0.9997872,0.00004751382,0.00001125697,0.00006560966,0.00004291459,0.00004564721],"domain_scores_gemma":[0.9994714,0.0002164475,0.0000366329,0.0001279838,0.0001188632,0.00002872154],"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":[0.0002254807,0.0001318924,0.0007454702,0.0001102482,0.0001159439,0.0000895707,0.00004628582,0.6226082,0.02376402,0.0103953,0.008646283,0.3331213],"study_design_scores_gemma":[0.000003603662,0.00001010954,0.0001293203,0.000005857684,0.000008037312,0.00001372295,0.000002804103,0.9934394,0.002996368,0.002640287,0.0007467251,0.000003736422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01893724,0.0008146493,0.9750064,0.0002275589,0.0001130486,0.00003246187,0.0002640424,0.002208274,0.00239633],"genre_scores_gemma":[0.606979,0.0009740671,0.3704583,0.000308751,0.0001252455,0.0001818111,0.001556147,0.0006679535,0.01874872],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007423937,"threshold_uncertainty_score":0.01476145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03164217300887807,"score_gpt":0.2798066813761105,"score_spread":0.2481645083672324,"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."}}