{"id":"W4400404142","doi":"10.55092/aias20240003","title":"Bridging domain gaps in CNNs: a comprehensive approach with adaptation and randomization strategies","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence and Autonomous Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bridging (networking); Domain adaptation; Computer science; Adaptation (eye); Randomization; Psychology; Artificial intelligence; Medicine; Randomized controlled trial; Neuroscience; Computer network","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":[0.001841181,0.001414816,0.001240505,0.0008576048,0.000543131,0.001173741,0.002464172,0.001953934,0.001953845],"category_scores_gemma":[0.004870032,0.0007249519,0.00129863,0.0007344549,0.001611174,0.002948039,0.003796387,0.002467482,0.0006721498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348218,"about_ca_system_score_gemma":0.001292942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002707544,"about_ca_topic_score_gemma":0.002181965,"domain_scores_codex":[0.9991229,0.0002524067,0.00005562265,0.0002692306,0.000198561,0.0001012909],"domain_scores_gemma":[0.9986695,0.0004815898,0.0001674846,0.0004716584,0.0001271076,0.00008261717],"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":[0.000155643,0.00008466391,0.001151058,0.0001122944,0.0001054059,0.0001947589,0.000161194,0.7812307,0.01076124,0.04311514,0.002836244,0.1600918],"study_design_scores_gemma":[0.000008205498,0.00003911668,0.0001240425,0.00001562165,0.0000149567,0.00006234647,0.0000183115,0.9717448,0.002526241,0.02333795,0.00209601,0.00001243686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01888705,0.0005777544,0.9765846,0.0003417448,0.00005968045,0.00007561906,0.00008438763,0.001062633,0.002326398],"genre_scores_gemma":[0.6873259,0.0008527306,0.3036295,0.0006642919,0.0001644213,0.000371731,0.0005196331,0.0005105095,0.005961288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002707544,"threshold_uncertainty_score":0.009782016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04160335173629109,"score_gpt":0.261799411560041,"score_spread":0.2201960598237499,"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."}}