{"id":"W3064600323","doi":"10.1109/lgrs.2020.3013026","title":"Component Interpretation for SAR Target Images Based on Deep Generative Model","year":2020,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Autoencoder; Pattern recognition (psychology); Residual; Component (thermodynamics); Synthetic aperture radar; Decoupling (probability); Deep learning; FEKO; Generative grammar; Data mining; Software; Algorithm","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":[0.00006618798,0.0001051854,0.00009233648,0.00004270037,0.0001539511,0.00007537068,0.00006814221,0.00002616534,2.705375e-7],"category_scores_gemma":[0.00001297163,0.00009724413,0.00002987698,0.0001026597,0.00008516145,0.00008795136,0.000007186487,0.00008191332,0.000001385047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002204365,"about_ca_system_score_gemma":0.000008829954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000579098,"about_ca_topic_score_gemma":4.427204e-7,"domain_scores_codex":[0.9994332,0.000007722035,0.000105451,0.0002066954,0.0000883323,0.0001585893],"domain_scores_gemma":[0.9997814,0.00002724264,0.00002517541,0.00008357449,0.00002751769,0.00005504965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008092045,0.000002651693,4.103936e-7,0.00003559521,0.000002178269,0.000001284501,0.0003959665,0.3953132,0.5478688,0.000005246115,0.001467639,0.05489888],"study_design_scores_gemma":[0.00008407491,0.00001851951,0.00000449056,0.00003026246,0.000005261548,0.000001326374,0.0000115666,0.8927999,0.1064991,0.000211181,0.0002228346,0.0001115699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02110706,0.00002058785,0.971427,0.006924032,0.00006499287,0.0001625391,0.000007943432,0.0002110902,0.00007480015],"genre_scores_gemma":[0.4642377,0.000004705095,0.5292783,0.006419181,0.00004078123,3.201446e-7,0.000004959748,0.00001068704,0.000003403991],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4974866,"threshold_uncertainty_score":0.3965501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01509634306526426,"score_gpt":0.2378478075080507,"score_spread":0.2227514644427865,"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."}}