{"id":"W2793797551","doi":"10.3390/rs10020351","title":"Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Università degli Studi di Trento; King Saud University","keywords":"Discriminator; Computer science; Categorization; Encoder; Artificial intelligence; Invariant (physics); Pattern recognition (psychology); Aerial image; Adversarial system; Domain (mathematical analysis); Computer vision; Image (mathematics); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001043689,0.001049428,0.0008164326,0.0006675237,0.0002438382,0.0006035607,0.001697057,0.0009838298,0.002084886],"category_scores_gemma":[0.001567794,0.0003825671,0.0008675589,0.0005796772,0.0007180554,0.001095762,0.00103412,0.001787623,0.0009655181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007860044,"about_ca_system_score_gemma":0.0005940112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003864215,"about_ca_topic_score_gemma":0.004546226,"domain_scores_codex":[0.9996036,0.0001155989,0.00001269692,0.0001358171,0.00008031579,0.00005194774],"domain_scores_gemma":[0.9995596,0.0001702036,0.00004446101,0.0001092847,0.00008734257,0.00002908739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001316243,0.0001072968,0.001247425,0.00007115238,0.0001338357,0.000100261,0.00005817333,0.6461753,0.009080399,0.01586484,0.00821065,0.318819],"study_design_scores_gemma":[0.00000356716,0.0000162698,0.00008594895,0.000002700979,0.000003420644,0.00002076793,0.000003593063,0.9944464,0.00107377,0.00373591,0.0006041246,0.000003513993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01132046,0.0003201932,0.9852818,0.000172843,0.00004556966,0.00005395824,0.0001363761,0.001457574,0.001211277],"genre_scores_gemma":[0.4701791,0.0004480405,0.517566,0.0005698253,0.000103189,0.0002151748,0.001740251,0.0003557416,0.008822618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003864215,"threshold_uncertainty_score":0.007683456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02214409529578646,"score_gpt":0.3132940359436259,"score_spread":0.2911499406478394,"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."}}