{"id":"W4401629926","doi":"10.1016/j.patcog.2024.110893","title":"Patient teacher can impart locality to improve lightweight vision transformer on small dataset","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Yunnan University","keywords":"Locality; Transformer; Computer science; Computer vision; Artificial intelligence; Engineering; Electrical engineering; Voltage","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001289857,0.0001945259,0.000126973,0.0001125123,0.0001077267,0.0001738001,0.0002961526,0.00006363727,0.00007233871],"category_scores_gemma":[0.000008187523,0.0001663463,0.00006113671,0.0003649557,0.000018226,0.0003020145,0.00007205697,0.0002359803,0.00122445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001071894,"about_ca_system_score_gemma":0.00002311043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004585505,"about_ca_topic_score_gemma":0.0001289282,"domain_scores_codex":[0.9984011,0.00006336745,0.000280921,0.0007152968,0.0002261531,0.0003131289],"domain_scores_gemma":[0.9991318,0.00008085497,0.00004123688,0.0005233681,0.00004360072,0.0001791124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008437554,0.00009341818,0.00002473979,0.00002343305,0.00000952835,0.00001106224,0.0002395208,0.00003059105,0.001714894,0.00005887989,0.006918297,0.9908672],"study_design_scores_gemma":[0.001207804,0.003955042,0.00307953,0.001162544,0.0001250758,0.0000919847,0.00007868704,0.1634679,0.2587169,0.0250688,0.5404092,0.002636606],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09486601,0.00004731562,0.8942562,0.006483673,0.0006949789,0.0009461804,0.001726469,0.0003892642,0.0005899647],"genre_scores_gemma":[0.9895008,0.00001881667,0.004071767,0.003915317,0.0002417423,0.000387256,0.001780191,0.00002988983,0.00005426439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9882306,"threshold_uncertainty_score":0.9995532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02714832255672208,"score_gpt":0.2850006522337626,"score_spread":0.2578523296770405,"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."}}