{"id":"W2612164513","doi":"","title":"Texture Transfer Based on Texture Descriptor Variations","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Texture filtering; Texture (cosmology); Texture compression; Projective texture mapping; Bidirectional texture function; Texture atlas; Artificial intelligence; Image texture; Computer science; Luminance; Displacement mapping; Computer vision; Texel; Pattern recognition (psychology); Orientation (vector space); Texture mapping; Mathematics; Image (mathematics); Geometry; Image segmentation","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003403973,0.0004988575,0.0004569241,0.0002211778,0.001035697,0.001793872,0.003796988,0.0004749011,0.0001621843],"category_scores_gemma":[0.001299234,0.0004807641,0.000380676,0.0002581279,0.0001937592,0.0003622168,0.0009586685,0.0009800743,0.00006752437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001271444,"about_ca_system_score_gemma":0.0005398048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004623061,"about_ca_topic_score_gemma":0.0006229314,"domain_scores_codex":[0.9929876,0.004266654,0.0004541833,0.001245708,0.000591538,0.0004543205],"domain_scores_gemma":[0.9917955,0.001101045,0.0003343294,0.004763104,0.001761667,0.0002442939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004296828,0.002307808,0.0006788929,0.0002676145,0.0003720424,0.00005467474,0.01298891,0.02463306,0.004552089,0.5117991,0.02700884,0.415294],"study_design_scores_gemma":[0.0005927596,9.760047e-7,0.00343467,0.001279347,0.0000604233,0.000002991407,0.00001264731,0.9434119,0.01120183,0.00607809,0.03322224,0.0007020961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002633826,0.0003249935,0.9297665,0.02707298,0.0007496488,0.0004916264,0.00008878069,0.0002601428,0.04098193],"genre_scores_gemma":[0.8024585,0.00011341,0.1900883,0.000575433,0.00009301955,0.0001011895,0.0002162924,0.00004655997,0.006307289],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9187789,"threshold_uncertainty_score":0.9997644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620584442376223,"score_gpt":0.2201415791930109,"score_spread":0.2039357347692487,"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."}}