{"id":"W2922263752","doi":"10.1016/j.neucom.2019.03.011","title":"Toward AI fashion design: An Attribute-GAN model for clothing match","year":2019,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Clothing; Computer science; Discriminator; Generator (circuit theory); Matching (statistics); Artificial intelligence; Similarity (geometry); Generative adversarial network; Collocation (remote sensing); Adversarial system; Generative grammar; Pattern recognition (psychology); Machine learning; Image (mathematics); Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0005734587,0.000221477,0.000262261,0.00007576695,0.0002624849,0.0004163574,0.0008545986,0.00007943229,0.000003487168],"category_scores_gemma":[0.00004639679,0.0002160314,0.0001120837,0.0002235172,0.00001409958,0.000921427,0.0002319789,0.0001957844,0.00003087383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003423374,"about_ca_system_score_gemma":0.00007122299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000582789,"about_ca_topic_score_gemma":9.977974e-7,"domain_scores_codex":[0.9980948,0.0001584202,0.0002959948,0.0007082704,0.0002305697,0.0005119226],"domain_scores_gemma":[0.9988452,0.0002618307,0.0001329454,0.0004943251,0.0001440911,0.0001215703],"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":[0.00000789541,0.00003359409,0.000185242,0.00001999433,0.000008035334,0.000003042111,0.0009272962,0.9628628,0.01091291,0.001943973,0.0004363152,0.02265882],"study_design_scores_gemma":[0.0003560586,0.0001520629,0.0001542818,0.00001929116,0.00000638954,0.000007510413,0.00002815525,0.9936345,0.00431066,0.0007875293,0.0002990086,0.0002446066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02627585,0.00001241048,0.9711862,0.0009476582,0.0006286061,0.0005951394,0.000001728834,0.0002522353,0.0001001963],"genre_scores_gemma":[0.7206813,0.000001379671,0.2775069,0.001474986,0.0002274655,0.000009574785,0.000002834499,0.00002260413,0.00007292823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6944055,"threshold_uncertainty_score":0.8809506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0666801175193587,"score_gpt":0.2795879814423403,"score_spread":0.2129078639229816,"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."}}