{"id":"W4387688000","doi":"10.48550/arxiv.2310.09213","title":"A Sampling-Based Domain Generalization Study with Diffusion Generative Models","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Domain (mathematical analysis); Generalization; Computer science; Artificial intelligence; Generative model; Pattern recognition (psychology); Subspace topology; Gaussian; Representation (politics); Sample (material); Statistical model; Algorithm; Mathematics; Generative grammar","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":[0.002546528,0.0008664726,0.0008138585,0.0006286028,0.0004599809,0.0009362953,0.001027164,0.001338812,0.001592489],"category_scores_gemma":[0.008295167,0.0006153924,0.001302229,0.0004638956,0.002148946,0.002116491,0.001754351,0.002358054,0.000216142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097716,"about_ca_system_score_gemma":0.0004738121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002853214,"about_ca_topic_score_gemma":0.001883795,"domain_scores_codex":[0.9994553,0.0002570275,0.00001634228,0.0001363376,0.00008368123,0.0000514183],"domain_scores_gemma":[0.9951454,0.00362777,0.0003585918,0.0005139404,0.0001943739,0.0001599009],"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":[0.00007512853,0.00003479436,0.001215735,0.00009453543,0.00006400078,0.000121522,0.0002539811,0.8695453,0.005000927,0.1075362,0.0007672886,0.01529059],"study_design_scores_gemma":[0.00000836048,0.00002543251,0.000156814,0.00001061175,0.000009738977,0.00005035503,0.00001715691,0.9725452,0.0007820665,0.02577421,0.000609809,0.00001021717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05683477,0.0008455775,0.9378369,0.0007166908,0.00004711307,0.00004947626,0.00006947511,0.0001591494,0.003440918],"genre_scores_gemma":[0.9052324,0.001053609,0.08787968,0.0003597422,0.0001516213,0.000126964,0.0001790397,0.000171628,0.004845405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002853214,"threshold_uncertainty_score":0.01346749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1104075731901478,"score_gpt":0.2087363050380569,"score_spread":0.0983287318479091,"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."}}