{"id":"W4283800850","doi":"10.1609/aaai.v36i7.20745","title":"Top-Down Deep Clustering with Multi-Generator GANs","year":2022,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Esri (Canada)","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Cluster analysis; Generator (circuit theory); Artificial intelligence; Data mining; Hierarchical clustering; Embedding; Classifier (UML); Brown clustering; Spectral clustering; Hierarchy; Pattern recognition (psychology); Machine learning; Fuzzy clustering; Canopy clustering algorithm; Power (physics)","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":[0.0007665969,0.001090534,0.000917786,0.000685874,0.0004238106,0.0007995109,0.001609999,0.0009197665,0.002729458],"category_scores_gemma":[0.001877567,0.000553078,0.0009889025,0.000694786,0.0007733683,0.001106112,0.001297945,0.001924607,0.00111807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00119985,"about_ca_system_score_gemma":0.0007547476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003765319,"about_ca_topic_score_gemma":0.007558174,"domain_scores_codex":[0.9995401,0.0001152338,0.00001658799,0.0001526853,0.0001150435,0.00006042798],"domain_scores_gemma":[0.9994121,0.0002355368,0.00005437093,0.0001468019,0.0001051658,0.00004608116],"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":[0.00007943568,0.00005001219,0.0005914349,0.00005571001,0.00006637967,0.00006349075,0.00006422053,0.8764809,0.006099348,0.02104335,0.00447678,0.09092886],"study_design_scores_gemma":[0.000002907653,0.00000779974,0.0000474838,0.000003224015,0.000003225737,0.00001269171,0.000004103887,0.9927471,0.001036427,0.005716742,0.0004147383,0.000003515765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009442532,0.0001944882,0.9869287,0.0001247892,0.00003089898,0.00003318159,0.00009637051,0.001253564,0.001895463],"genre_scores_gemma":[0.5493863,0.0003468246,0.4370014,0.0005381896,0.00009049835,0.00018504,0.001249317,0.0008660604,0.01033627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003765319,"threshold_uncertainty_score":0.009130895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351786318544438,"score_gpt":0.2105684973677443,"score_spread":0.1970506341822999,"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."}}