{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001055512,0.001062064,0.001114969,0.0006495087,0.0004383925,0.0008199203,0.002733981,0.001670032,0.005277773],"category_scores_gemma":[0.001695174,0.0006365724,0.001130879,0.00083472,0.000730218,0.00142315,0.00280419,0.00287442,0.002097813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000764947,"about_ca_system_score_gemma":0.0007044217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005304856,"about_ca_topic_score_gemma":0.006877512,"domain_scores_codex":[0.999532,0.0001132496,0.00001676072,0.0001072116,0.0001769118,0.00005385224],"domain_scores_gemma":[0.9995648,0.0002063313,0.00002532241,0.00008276256,0.00009510772,0.00002561154],"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":[0.0002140663,0.00008977449,0.0002592712,0.00008000586,0.0001268898,0.00008924779,0.00004112026,0.6516483,0.009384347,0.01862611,0.009467217,0.3099737],"study_design_scores_gemma":[0.000002916315,0.00000808655,0.00003214,0.000003417383,0.000005307519,0.00001477178,0.000001505835,0.9939911,0.001428476,0.003725295,0.000782361,0.000004607098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001476414,0.000149648,0.9961181,0.00005706678,0.00004149136,0.00002644272,0.00008291203,0.001131235,0.0009167986],"genre_scores_gemma":[0.1786281,0.0006726308,0.8025583,0.000518591,0.0001187408,0.0002492792,0.00126381,0.0009736033,0.01501704],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005304856,"threshold_uncertainty_score":0.01765597,"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."}}