{"id":"W2910601191","doi":"10.48550/arxiv.1901.02199","title":"FIGR: Few-shot Image Generation with Reptile","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"","keywords":"Shot (pellet); Computer science; Artificial intelligence; Benchmark (surveying); MNIST database; Image (mathematics); Novelty; Generative grammar; Set (abstract data type); Class (philosophy); Limiting; Field (mathematics); Domain (mathematical analysis); One shot; Deep learning; Machine learning; Computer vision; Pattern recognition (psychology); Cartography; Mathematics; Geography; Engineering","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":[0.000739172,0.0007570793,0.000394231,0.0003687297,0.0001946698,0.000581732,0.001475318,0.001011051,0.00582333],"category_scores_gemma":[0.00194276,0.0002962287,0.0006464857,0.0002262917,0.0005614902,0.000803624,0.001089501,0.001364541,0.00133002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005612171,"about_ca_system_score_gemma":0.0003410176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002094998,"about_ca_topic_score_gemma":0.003529534,"domain_scores_codex":[0.999712,0.00007166195,0.000009344747,0.00009487159,0.00008241062,0.00002960002],"domain_scores_gemma":[0.9995366,0.0002104694,0.00002868126,0.0001486807,0.00004842407,0.00002712999],"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":[0.0003558935,0.0002118221,0.00158989,0.0002681318,0.000154962,0.000372293,0.0001400384,0.7019465,0.02475958,0.01627089,0.02973061,0.2241994],"study_design_scores_gemma":[0.00002381628,0.00005965749,0.0001688071,0.00001080413,0.000007161981,0.00009064413,0.000007362934,0.9841751,0.006817555,0.005026516,0.003601545,0.00001093074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05067383,0.0009171973,0.9141786,0.0007274827,0.000349231,0.0003181597,0.001512157,0.01816151,0.01316185],"genre_scores_gemma":[0.6241747,0.0003260946,0.3587202,0.0008461176,0.00008310108,0.0003316614,0.003400445,0.001470819,0.01064679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00582333,"threshold_uncertainty_score":0.019481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0786742616169836,"score_gpt":0.1812348152687094,"score_spread":0.1025605536517258,"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."}}