{"id":"W4407264220","doi":"10.1016/j.neucom.2025.129572","title":"MDANet: A multi-stage domain adaptation framework for generalizable low-light image enhancement","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs","funders":"Shenzhen Fundamental Research and Discipline Layout project","keywords":"Computer science; Adaptation (eye); Image (mathematics); Domain adaptation; Artificial intelligence; Domain (mathematical analysis); Stage (stratigraphy); Image enhancement; Computer vision; Pattern recognition (psychology); Mathematics; Optics; Physics; Geology","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":[0.0004609694,0.0007571782,0.0005543932,0.0004217879,0.0002185271,0.0004944208,0.001239655,0.0007457272,0.003896832],"category_scores_gemma":[0.0007220329,0.0003397522,0.0006288646,0.0003895403,0.0002234705,0.0006061488,0.0007729764,0.001337813,0.001526788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003207582,"about_ca_system_score_gemma":0.0005041218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00485662,"about_ca_topic_score_gemma":0.01071556,"domain_scores_codex":[0.9998792,0.00002205792,0.000005308279,0.0000337525,0.00004189341,0.00001778365],"domain_scores_gemma":[0.9998374,0.00004616351,0.00001297008,0.0000311053,0.00006034205,0.00001202008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002709948,0.0001676232,0.0005375988,0.0001500652,0.0001467791,0.0001086754,0.00005305697,0.1604089,0.08941196,0.006407132,0.01157883,0.7307584],"study_design_scores_gemma":[0.000008364162,0.00002493677,0.0002311194,0.000006472983,0.00001414029,0.00004283796,0.000006018564,0.9790852,0.01472375,0.001875004,0.003972626,0.00000964512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002589756,0.0001273824,0.9945583,0.00003286236,0.00002863461,0.00002034051,0.00006761812,0.002108459,0.0004666404],"genre_scores_gemma":[0.1000309,0.0003538994,0.8903487,0.0001964028,0.00005016092,0.0001126629,0.0005822832,0.0006172018,0.007707805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00485662,"threshold_uncertainty_score":0.01303619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02864878368878971,"score_gpt":0.3190896335784755,"score_spread":0.2904408498896858,"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."}}