{"id":"W4312525628","doi":"10.33965/mccsis2022_202206l013","title":"MULTI-MODALITY IMAGE SUPER-RESOLUTION USING GENERATIVE ADVERSARIAL NETWORKS","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Image translation; Generative grammar; Image (mathematics); Computer science; Artificial intelligence; Translation (biology); Modality (human–computer interaction); Generative adversarial network; Superresolution; Deep learning; Computer vision; Resolution (logic)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004033327,0.0001430585,0.0001381779,0.00007688118,0.000720648,0.0001460238,0.0007765636,0.00003453116,0.00005642523],"category_scores_gemma":[0.0000443855,0.0001479673,0.00005719741,0.0004424032,0.00007662235,0.001232727,0.001292765,0.0002806883,0.000002462729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002939137,"about_ca_system_score_gemma":0.0001042918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000128609,"about_ca_topic_score_gemma":0.000009239556,"domain_scores_codex":[0.998527,0.0001965674,0.0002087519,0.0004760729,0.000276309,0.0003153135],"domain_scores_gemma":[0.9992548,0.00003420531,0.000090817,0.0004534579,0.0001082619,0.00005848161],"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":[0.000105457,0.0008581656,0.000472928,0.00002684838,0.00006499003,0.000145267,0.00237915,0.3091039,0.614598,0.03556846,0.006965438,0.02971134],"study_design_scores_gemma":[0.0002743055,0.00004020885,0.00005258257,0.000002423706,0.000003684444,0.00002102454,0.00003477291,0.9913685,0.004397885,0.003042036,0.0005714092,0.0001911791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006357979,0.0000940325,0.9972057,0.0004945977,0.0003737514,0.0001845826,0.000003569672,0.0007590742,0.0002488673],"genre_scores_gemma":[0.1485232,0.000002150475,0.850807,0.0004092244,0.00008477865,0.00003244034,0.000005137197,0.00001083135,0.0001252706],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6822646,"threshold_uncertainty_score":0.6033933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03513344909082635,"score_gpt":0.3083428375710157,"score_spread":0.2732093884801893,"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."}}