{"id":"W4402261023","doi":"10.1109/igarss53475.2024.10641390","title":"Loss Functions Analysis of Performance Improvements in Single-Image Super-Resolution","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Image (mathematics); Computer vision; Artificial intelligence","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":[0.000195653,0.00009181074,0.0001446257,0.000720423,0.00004009284,0.0001091685,0.0003575283,0.00003317581,0.0000259884],"category_scores_gemma":[0.000023047,0.00008281212,0.00006546483,0.002889332,0.00005678229,0.001569227,0.0001734339,0.00009892972,0.0000124585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001017916,"about_ca_system_score_gemma":0.00004095773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003629554,"about_ca_topic_score_gemma":0.00003319282,"domain_scores_codex":[0.9990495,0.00001599772,0.0002522725,0.0003189865,0.0001821448,0.0001811339],"domain_scores_gemma":[0.9994738,0.00003283252,0.00003444537,0.0003488305,0.00008421676,0.00002589825],"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":[0.00001607212,0.0005161708,0.009428548,0.0003650888,0.0003515486,0.00003352299,0.001212481,0.001298549,0.5195864,0.01356812,0.0006509569,0.4529725],"study_design_scores_gemma":[0.00006010483,0.00007696251,0.002455389,0.00005165076,0.00004648519,0.000001810726,0.00001487045,0.9616852,0.03384827,0.0008994304,0.0007453512,0.0001144461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02920774,0.0001498777,0.9673731,0.0001992928,0.00008947842,0.00007080107,0.00000280116,0.0004397508,0.002467136],"genre_scores_gemma":[0.7127799,0.00001282627,0.2866873,0.00003411585,0.000006700975,0.00001354435,0.000004053714,0.000004670293,0.0004568881],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9603867,"threshold_uncertainty_score":0.337698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0157043534662252,"score_gpt":0.2755171299058316,"score_spread":0.2598127764396064,"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."}}