{"id":"W2984179350","doi":"10.1117/12.2538012","title":"Using transfer learning technique for SAR automatic target recognition","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Synthetic aperture radar; Artificial intelligence; Computer science; Transfer of learning; Pattern recognition (psychology); Automatic target recognition; Convolutional neural network; Support vector machine; Novelty detection; Novelty; Deep learning; Contextual image classification; Scheme (mathematics); Machine learning; Image (mathematics); 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":[0.0001414595,0.0001235528,0.0001431119,0.00009676925,0.0000339006,0.00001676385,0.00005704398,0.00007106305,0.0001990319],"category_scores_gemma":[0.00001889957,0.0001296872,0.00005470769,0.00009487361,0.000009848255,0.0002287482,0.000007490341,0.0001502442,0.00003695509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007370723,"about_ca_system_score_gemma":0.000007025696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002789263,"about_ca_topic_score_gemma":3.244515e-7,"domain_scores_codex":[0.999428,0.00001274171,0.0001596728,0.0001316266,0.00006553558,0.0002024406],"domain_scores_gemma":[0.9997633,0.00004652383,0.000009397676,0.0001211565,0.00003337174,0.00002624256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004808362,0.00001248878,0.000214984,0.0004196029,0.00002380008,0.000001462834,0.0001182135,0.03850213,0.9446267,0.0002192466,0.0002567727,0.0155998],"study_design_scores_gemma":[0.0001341948,0.0000296589,0.000008028713,0.00008469857,0.000008079537,0.00001217342,0.00002195995,0.4852234,0.5065523,0.004938854,0.002786526,0.0002001667],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04631039,0.00002810295,0.946647,0.00001069515,0.00007410952,0.0007861233,0.000004526982,0.002774126,0.003364963],"genre_scores_gemma":[0.3893734,0.000004609676,0.610383,0.00002155282,0.00001903008,0.00004929733,0.00001341639,0.00005359214,0.00008211411],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4467212,"threshold_uncertainty_score":0.5288491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02358214655515534,"score_gpt":0.269053875957172,"score_spread":0.2454717294020167,"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."}}