{"id":"W3214459635","doi":"10.1109/iceccme52200.2021.9591087","title":"Facial Animation Using CycleGAN","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Anime; Adversarial system; Artificial intelligence; Generative grammar; Animation; Translation (biology); Image translation; Face (sociological concept); Deep learning; Field (mathematics); Transformation (genetics); Image (mathematics); Natural language processing; Computer vision; Pattern recognition (psychology); Computer graphics (images); Linguistics; 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":[0.0002015659,0.0002196141,0.0002236334,0.0001894646,0.0002639163,0.0005695567,0.001178475,0.00009216287,0.00004845799],"category_scores_gemma":[0.00005290077,0.0002418111,0.00009084773,0.0004420579,0.00003153168,0.0004549437,0.0006672995,0.0003705974,0.00001544578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000156739,"about_ca_system_score_gemma":0.0002123384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002446264,"about_ca_topic_score_gemma":0.00000969325,"domain_scores_codex":[0.9985237,0.0001153807,0.0003322409,0.0004344291,0.0002963917,0.0002978457],"domain_scores_gemma":[0.9983094,0.0001556878,0.000118172,0.0008863323,0.0004139194,0.0001165174],"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":[0.000005309164,0.0001732834,0.00001315685,0.000005900463,0.0001361593,0.000007085703,0.0001493881,0.02587901,0.01883904,0.8458037,0.0001987014,0.1087892],"study_design_scores_gemma":[0.0002574873,0.0000734085,0.00006846767,0.00004733572,0.00001056438,0.00002461295,0.00001337624,0.9819305,0.001323605,0.001254741,0.01475296,0.000242937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002231104,0.0006368897,0.9927748,0.002439664,0.0005581722,0.0001116823,0.00001120588,0.00009036712,0.00114607],"genre_scores_gemma":[0.6866096,0.002104512,0.3106964,0.000186135,0.0002158603,0.00001630161,0.00005340217,0.00001667458,0.0001011111],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9560515,"threshold_uncertainty_score":0.9860772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03616989701566346,"score_gpt":0.2700761430426632,"score_spread":0.2339062460269997,"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."}}