{"id":"W2765932136","doi":"10.1145/3123266.3123334","title":"Metric-based Generative Adversarial Network","year":2017,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"New York University Abu Dhabi; Canadian Institute for Advanced Research","keywords":"Discriminator; Margin (machine learning); Metric (unit); Generator (circuit theory); Computer science; Artificial intelligence; Sample (material); Feature (linguistics); Feature vector; Generative grammar; Energy (signal processing); Focus (optics); Pattern recognition (psychology); Adversarial system; Machine learning; Mathematics; Power (physics); Statistics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004102693,0.0001840493,0.0002221116,0.00006075585,0.001232131,0.0009354185,0.001687934,0.00007277508,0.0001422003],"category_scores_gemma":[0.0002213309,0.0001485851,0.0001319335,0.000183657,0.00009460113,0.0009052407,0.0003883477,0.0001100903,0.0001180099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003337386,"about_ca_system_score_gemma":0.000107488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001304267,"about_ca_topic_score_gemma":0.00006406342,"domain_scores_codex":[0.9985597,0.000112775,0.0001933972,0.000462765,0.0002628612,0.000408535],"domain_scores_gemma":[0.9980801,0.0001327038,0.0001879112,0.001334278,0.0001322515,0.0001327359],"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":[0.00006967771,0.00021038,0.003839269,0.000008294185,0.0002557658,0.0001020299,0.0002095462,0.3550802,0.001554406,0.2467314,0.1836893,0.2082497],"study_design_scores_gemma":[0.0008653381,0.0001006638,0.00442125,0.000009404896,0.00001614487,0.000001433085,0.000005458814,0.9463723,0.01151885,0.003872549,0.0324455,0.0003711315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001394353,0.00006800343,0.9577593,0.002738673,0.001791021,0.0001427698,0.000001380579,0.000109396,0.03725007],"genre_scores_gemma":[0.6522967,0.000005824717,0.3445923,0.0009669358,0.001125242,0.00001096524,0.000001519925,0.00000809851,0.000992337],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6521573,"threshold_uncertainty_score":0.9476683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02192488872617899,"score_gpt":0.255753912171707,"score_spread":0.233829023445528,"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."}}