{"id":"W3037016570","doi":"10.1609/aaai.v34i07.6914","title":"EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Shenzhen University; National Natural Science Foundation of China","keywords":"Computer science; Statistic; Artificial intelligence; Swap (finance); Pattern recognition (psychology); Feature vector; Feature (linguistics); Matching (statistics); Feature extraction; Graphics; Image (mathematics); Extractor; Mathematics; Computer graphics (images)","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":[0.001125071,0.00123026,0.0008429037,0.0007293609,0.0004756375,0.0007561996,0.00202068,0.001361083,0.004655004],"category_scores_gemma":[0.002402561,0.0003915379,0.0008131283,0.0007329027,0.0007642887,0.001627984,0.001774732,0.001894616,0.001455744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008195813,"about_ca_system_score_gemma":0.0006185754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001966306,"about_ca_topic_score_gemma":0.002481201,"domain_scores_codex":[0.9995331,0.00008959515,0.00001936645,0.0001479419,0.0001390284,0.00007096325],"domain_scores_gemma":[0.9995934,0.0001329591,0.00005136713,0.0001127815,0.00007332517,0.00003623613],"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":[0.0003047362,0.0002104348,0.0009715654,0.0001016656,0.0001117843,0.0002660548,0.0001451194,0.4532047,0.01967808,0.0228548,0.01132163,0.4908293],"study_design_scores_gemma":[0.00001043835,0.00003680955,0.00009303367,0.000004692478,0.000006642424,0.00004401251,0.000007032103,0.9878256,0.002555912,0.008255137,0.001154116,0.000006668626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01875594,0.0002732888,0.9733594,0.0002120286,0.00009592269,0.00008805769,0.0002220992,0.003103645,0.003889655],"genre_scores_gemma":[0.594126,0.0003311895,0.3843115,0.0007745231,0.0001484411,0.0003990146,0.001538881,0.0006306248,0.01773975],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004655004,"threshold_uncertainty_score":0.01557255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07239168392874401,"score_gpt":0.2596019731351739,"score_spread":0.1872102892064299,"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."}}