{"id":"W2905439256","doi":"10.1109/lgrs.2018.2884898","title":"Synthetic Aperture Radar Image Generation With Deep Generative Models","year":2018,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Synthetic aperture radar; Autoencoder; Computer science; Artificial intelligence; Radar imaging; Deep learning; Generative grammar; Image (mathematics); Generative model; Pattern recognition (psychology); Computer vision; Inverse synthetic aperture radar; Radar; Telecommunications","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":[0.0001027175,0.000176127,0.0001276934,0.00008819441,0.0002385503,0.00009828048,0.00008598562,0.00004259965,8.457221e-7],"category_scores_gemma":[0.00001276117,0.0001412542,0.00001878617,0.000183221,0.0005071237,0.0004124066,0.00001473455,0.0001351088,0.000004980626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004971275,"about_ca_system_score_gemma":0.000009620385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003913914,"about_ca_topic_score_gemma":0.00003139934,"domain_scores_codex":[0.9990809,0.00002289933,0.0001121517,0.000315972,0.0001730677,0.0002950392],"domain_scores_gemma":[0.999596,0.00002368725,0.00002763188,0.0002366282,0.0000536534,0.0000624028],"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":[0.000002574768,0.000001203496,4.253147e-7,0.000006474924,0.000004951059,0.00002441034,0.0005265203,0.00219282,0.9489318,0.000007059002,0.0004570151,0.0478447],"study_design_scores_gemma":[0.00007065655,0.00002551267,0.00000463326,0.00004331205,0.000007639673,0.0001706932,0.00002273649,0.8510733,0.1477138,0.0002522698,0.0004146529,0.0002008192],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1496182,0.00005642147,0.8484833,0.00090929,0.0002120857,0.0001007424,0.000001464372,0.0003305518,0.000288026],"genre_scores_gemma":[0.3508618,0.0000362254,0.6467942,0.002036388,0.0002226247,1.204262e-7,0.000001743194,0.00002667201,0.00002019521],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8488804,"threshold_uncertainty_score":0.5760177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111880974607109,"score_gpt":0.2159252705074099,"score_spread":0.2048064607613388,"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."}}