{"id":"W4220917622","doi":"10.1109/tnnls.2022.3153955","title":"Image Matting With Deep Gaussian Process","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Science Foundation of Shandong Province; Taishan Scholar Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Deep learning; Kernel (algebra); Image (mathematics); Computer science; Pixel; Scalability; Pattern recognition (psychology); Process (computing); Set (abstract data type); Gaussian process; Gaussian; Computer vision; Machine learning; Mathematics; Physics","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":[0.000641812,0.0006886215,0.000637559,0.0006355464,0.0002382317,0.001100594,0.00118139,0.001178734,0.003601927],"category_scores_gemma":[0.002238968,0.0004327765,0.001072301,0.0007718027,0.0009722438,0.001939283,0.001329276,0.00201249,0.001083156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091578,"about_ca_system_score_gemma":0.0008277995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00416987,"about_ca_topic_score_gemma":0.004190591,"domain_scores_codex":[0.9997298,0.00004621929,0.000009685854,0.00007273957,0.0001068845,0.00003468406],"domain_scores_gemma":[0.9994178,0.0002186364,0.00006096426,0.0001471705,0.0001103993,0.00004498777],"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":[0.0001025441,0.00004466767,0.0005303283,0.00009760188,0.00006018448,0.0001746292,0.0001181311,0.7795095,0.01581685,0.07240959,0.003471547,0.1276645],"study_design_scores_gemma":[0.000003004608,0.000007163555,0.00003048862,0.000002050032,0.000002327685,0.00001588227,0.000002467493,0.9893135,0.001588496,0.008547072,0.0004842103,0.00000324315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005242774,0.00008344884,0.9925053,0.0001570282,0.00002470333,0.00001289799,0.00003673456,0.0007746614,0.001162502],"genre_scores_gemma":[0.3944485,0.0003920386,0.5935054,0.0003188355,0.0001238724,0.00007867123,0.0002954917,0.0006469279,0.01019031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00416987,"threshold_uncertainty_score":0.01204967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007203179155005628,"score_gpt":0.2251430017639678,"score_spread":0.2179398226089622,"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."}}