{"id":"W4401808768","doi":"10.1007/s00521-024-10353-5","title":"Vision transformers in domain adaptation and domain generalization: a study of robustness","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computational Science and Engineering; Robustness (evolution); Computer science; Domain adaptation; Transformer; Artificial intelligence; Generalization; Machine learning; Mathematics; Mathematical analysis; Electrical engineering","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":[0.009021955,0.0009979897,0.001671384,0.001847773,0.0008080732,0.002909842,0.002656494,0.002451026,0.002501278],"category_scores_gemma":[0.05549176,0.0008134139,0.001568575,0.001224274,0.006071928,0.007208166,0.005137098,0.004178667,0.0002310665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001493884,"about_ca_system_score_gemma":0.000925709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001940295,"about_ca_topic_score_gemma":0.0006911887,"domain_scores_codex":[0.9968745,0.001427517,0.0001285112,0.0007655917,0.000572308,0.0002316414],"domain_scores_gemma":[0.9459649,0.0435023,0.002967515,0.005080357,0.001466624,0.001018309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000430784,0.0001259437,0.002016329,0.0002845829,0.0002959507,0.0002386371,0.0004573759,0.2757611,0.008058093,0.6526757,0.00139295,0.05826254],"study_design_scores_gemma":[0.00002590021,0.0001263572,0.000669708,0.00002636117,0.00004665111,0.0001518409,0.00007184129,0.6448909,0.002625779,0.3508604,0.000475162,0.00002907671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08251169,0.001472943,0.9085649,0.0009133916,0.00005955627,0.00006658419,0.00008110188,0.0003048817,0.006024986],"genre_scores_gemma":[0.9361645,0.001445519,0.05851613,0.0002839958,0.0002208646,0.00009518413,0.0001339425,0.0002218289,0.002918034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009021955,"threshold_uncertainty_score":0.04771322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02158830102822108,"score_gpt":0.2907351512044086,"score_spread":0.2691468501761876,"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."}}