{"id":"W4293863534","doi":"10.1109/siu55565.2022.9864747","title":"Game Character Generation with Generative Adversarial Networks","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Generative grammar; Task (project management); Metric (unit); Artificial intelligence; Variation (astronomy); Adversarial system; Character (mathematics); Machine learning; The Internet; Scratch; Process (computing); Generative adversarial network; Generative Design; Deep learning; World Wide Web; Programming language","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":[0.0006277849,0.0009532829,0.0004500015,0.0004471786,0.0002063784,0.0005407182,0.0008299841,0.0007621352,0.003115498],"category_scores_gemma":[0.001878279,0.0004711984,0.0006661846,0.0002828279,0.0005407337,0.000703332,0.0008438986,0.001392301,0.0007718803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006668764,"about_ca_system_score_gemma":0.0003418266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003037373,"about_ca_topic_score_gemma":0.004177236,"domain_scores_codex":[0.9997348,0.0000816763,0.000008050944,0.00008519779,0.00005752096,0.00003260281],"domain_scores_gemma":[0.9993863,0.0004093819,0.0000377654,0.00006760914,0.00007186462,0.00002704901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008151632,0.00004998562,0.0004019145,0.00004484285,0.00003545177,0.00008693967,0.00004708301,0.921716,0.003562175,0.005085649,0.002705628,0.06618276],"study_design_scores_gemma":[0.000003420428,0.000009395649,0.00003556247,0.000002788096,0.000002001859,0.0000108105,0.000002927889,0.9975237,0.0006567918,0.001414785,0.0003356648,0.000002236823],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03783383,0.000409427,0.9514914,0.0003630344,0.0001469236,0.0001553096,0.0002759608,0.002300418,0.007023669],"genre_scores_gemma":[0.7759295,0.0002323026,0.2088121,0.0004816764,0.00006914368,0.0002704957,0.0009344498,0.000467716,0.01280248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003115498,"threshold_uncertainty_score":0.01042241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02842263624741773,"score_gpt":0.2467887620001516,"score_spread":0.2183661257527339,"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."}}