{"id":"W4404399962","doi":"10.1007/978-981-96-0026-7_10","title":"EBcGAN: An Edge-Based Conditional Generative Adversarial Network for Image Fusion","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Adversarial system; Generative grammar; Image (mathematics); Artificial intelligence; Enhanced Data Rates for GSM Evolution; Generative adversarial network; Image fusion; Computer vision; Fusion; Theoretical computer science; Linguistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002790996,0.0004366724,0.0003528954,0.000358128,0.0001762128,0.0001908653,0.0005861562,0.0002784444,0.0000968269],"category_scores_gemma":[0.00003419746,0.0004224589,0.0001174648,0.0002223521,0.000431681,0.0003230026,0.0001754549,0.0005858736,0.0000205377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003057326,"about_ca_system_score_gemma":0.000182495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001775847,"about_ca_topic_score_gemma":0.00002472,"domain_scores_codex":[0.9980549,0.000009649016,0.0003288929,0.0007718501,0.0004007259,0.0004339363],"domain_scores_gemma":[0.9989244,0.0002726228,0.00006885817,0.0004394424,0.0001854724,0.0001092273],"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":[0.00002120919,0.00001135469,7.380126e-7,0.000104161,0.00001260237,0.00005784384,0.0001132079,0.8883595,0.007880323,0.005985684,0.001357792,0.09609561],"study_design_scores_gemma":[0.0001846512,0.0001345379,0.000002121909,0.0002334418,0.00001151349,0.000008883924,7.068826e-8,0.7205898,0.01898641,0.2536742,0.005758512,0.0004158432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001643855,0.0003258153,0.9942304,0.0001154962,0.002573111,0.0006644253,0.0001337212,0.0006533833,0.001287218],"genre_scores_gemma":[0.007828133,0.00002063147,0.9875919,0.0006433484,0.003236028,0.00006255532,0.0002664545,0.0001116728,0.0002392431],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2476885,"threshold_uncertainty_score":0.9998227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068638539994986,"score_gpt":0.2520217452880604,"score_spread":0.2413353598881105,"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."}}