{"id":"W3180352537","doi":"","title":"FAST-ADAM in Semi-Supervised Generative Adversarial Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Internet, Broadcasting and Communication","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Discriminator; Generator (circuit theory); Computer science; Benchmark (surveying); Generative grammar; Adversarial system; Stability (learning theory); Machine learning; Artificial intelligence; Convergence (economics); Artificial neural network; Power (physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001520236,0.001178959,0.001016395,0.0002900596,0.0002723241,0.0006123019,0.001046487,0.001032967,0.001413855],"category_scores_gemma":[0.002993471,0.0006082855,0.000647973,0.0002772469,0.001356737,0.000915735,0.00118371,0.002287834,0.0005160769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006637037,"about_ca_system_score_gemma":0.0007482455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001884385,"about_ca_topic_score_gemma":0.001844571,"domain_scores_codex":[0.9993684,0.0002924605,0.00003288778,0.0001460643,0.0001093056,0.00005085198],"domain_scores_gemma":[0.9987326,0.0008885529,0.00009739532,0.0001164632,0.0001179161,0.00004708814],"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":[0.00005518781,0.00001702626,0.0003741314,0.00005942022,0.00003609715,0.00006471619,0.00004186113,0.9610426,0.001786177,0.01266042,0.001101236,0.0227612],"study_design_scores_gemma":[0.000003220168,0.0000116123,0.00003233479,0.000004352542,0.000002612436,0.00001478134,0.000001613495,0.995282,0.000523873,0.003814466,0.0003060469,0.000003064693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01036701,0.0006176806,0.985976,0.000261777,0.00004672854,0.00003095731,0.00004930535,0.0007217411,0.001928885],"genre_scores_gemma":[0.7422184,0.0009414252,0.2483383,0.0004341582,0.000117529,0.000232112,0.0003451163,0.000406923,0.006966032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001884385,"threshold_uncertainty_score":0.008039832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.010661342132263,"score_gpt":0.2380858389611965,"score_spread":0.2274244968289335,"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."}}