{"id":"W4360604838","doi":"10.1109/icnc57223.2023.10074146","title":"Evaluating Generative Adversarial Networks: A Topological Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Persistent homology; Metric (unit); Computer science; Topology (electrical circuits); Topological data analysis; Convolution (computer science); Manifold (fluid mechanics); Generative grammar; Algebraic number; Algebraic topology; Artificial neural network; Adversarial system; Artificial intelligence; Theoretical computer science; Mathematics; Algorithm; Pure mathematics; Homotopy; Combinatorics","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":[0.0008089484,0.000112483,0.0001911517,0.0001871905,0.0001915803,0.0001405264,0.0008339594,0.00008243402,0.000231466],"category_scores_gemma":[0.0003344173,0.00007128585,0.00010676,0.003551957,0.00006086922,0.0002426413,0.0007239413,0.00012569,0.000281147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001804492,"about_ca_system_score_gemma":0.00001970068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004333164,"about_ca_topic_score_gemma":0.000002197711,"domain_scores_codex":[0.9984536,0.0001602996,0.0002009744,0.0005026591,0.0003241071,0.0003584002],"domain_scores_gemma":[0.9991593,0.0002529875,0.00004817728,0.0003892739,0.00005082646,0.00009937211],"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":[0.00001110285,0.0001420721,0.001001648,0.000002791321,0.0001051986,0.00004102261,0.0002180228,0.1163277,0.00005699689,0.7241545,0.01489822,0.1430407],"study_design_scores_gemma":[0.0001889677,0.0001122079,0.001010502,5.595273e-7,0.000009114588,0.000003552377,0.00007164237,0.9799709,0.00001891396,0.01764333,0.0008457627,0.000124506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006739242,0.0000419466,0.9801133,0.001094203,0.000233996,0.00008925405,0.000002015463,0.0005255935,0.01116047],"genre_scores_gemma":[0.7171788,0.00003423979,0.2782334,0.001062283,0.0004404595,0.0000423472,0.00005819235,0.000004457646,0.002945859],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8636432,"threshold_uncertainty_score":0.361367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1127029968944292,"score_gpt":0.3515251318734108,"score_spread":0.2388221349789817,"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."}}