{"id":"W3109507914","doi":"10.1007/s00530-020-00714-0","title":"A TextCNN and WGAN-gp based deep learning frame for unpaired text style transfer in multimedia services","year":2020,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Yunnan Provincial Science and Technology Department","keywords":"Computer science; Style (visual arts); Sentence; Natural language processing; Encoder; Multimedia; Representation (politics); Artificial intelligence; Frame (networking); Information retrieval; Feature (linguistics); Linguistics; Telecommunications","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":[0.0003555061,0.000649346,0.0005633901,0.0004696189,0.0003428828,0.0004999285,0.001033384,0.001163667,0.005335358],"category_scores_gemma":[0.0007879434,0.0002369138,0.0005466567,0.0005603045,0.0003389785,0.000800569,0.0009071235,0.001460405,0.002177292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007295576,"about_ca_system_score_gemma":0.0007599034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01022132,"about_ca_topic_score_gemma":0.01066013,"domain_scores_codex":[0.9997962,0.00003053265,0.000008171401,0.00006109993,0.0000661731,0.0000378405],"domain_scores_gemma":[0.9998459,0.00002986786,0.000008783186,0.00003031461,0.00006715169,0.00001803917],"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":[0.0004766762,0.0002235874,0.0005498114,0.0001080498,0.00005371027,0.0002604538,0.00005964265,0.3118247,0.04011465,0.01699596,0.01321237,0.6161203],"study_design_scores_gemma":[0.000005813986,0.00002370226,0.00008598362,0.000005562599,0.000007378529,0.00002049755,0.000004059973,0.9914805,0.005207244,0.001794403,0.001359636,0.000005204908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02108348,0.0004302568,0.9696427,0.0003441906,0.0002571291,0.00009870523,0.0004180072,0.002099741,0.005625784],"genre_scores_gemma":[0.6009065,0.0006874144,0.3623905,0.0007368362,0.0002266171,0.0002252282,0.001525791,0.0004023214,0.03289889],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01022132,"threshold_uncertainty_score":0.02032369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602321042832459,"score_gpt":0.2154907238792844,"score_spread":0.1994675134509598,"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."}}