{"id":"W2962860923","doi":"10.1109/iccv.2019.00285","title":"Lifelong GAN: Continual Learning for Conditional Image Generation","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Forgetting; Lifelong learning; Computer science; Artificial intelligence; Artificial neural network; Generative grammar; Machine learning; Image (mathematics); Task (project management); Generative model; Cognitive psychology; Engineering; Psychology","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":[0.001245321,0.0008899863,0.0005677493,0.0002837903,0.0002756499,0.0006726435,0.002009327,0.00118877,0.003695828],"category_scores_gemma":[0.003311648,0.0004289187,0.0004968878,0.0002858058,0.0009721043,0.001621083,0.001592044,0.002244388,0.00082837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000843234,"about_ca_system_score_gemma":0.0006249677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002159017,"about_ca_topic_score_gemma":0.004327985,"domain_scores_codex":[0.999622,0.000122569,0.00001308051,0.000118078,0.0000796493,0.00004460335],"domain_scores_gemma":[0.9986985,0.0006369195,0.00008137367,0.0003625474,0.0001417457,0.00007885494],"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":[0.000203443,0.000173082,0.001563511,0.0001416479,0.00006713651,0.0002205949,0.000155366,0.7787006,0.01165548,0.02948809,0.009194057,0.1684369],"study_design_scores_gemma":[0.000005530158,0.0000168892,0.00005954832,0.000004525432,0.000002386262,0.00002436516,0.000003595328,0.9906864,0.00170143,0.006974803,0.0005165813,0.000003954496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01884131,0.000288692,0.9755591,0.0002549519,0.00004867133,0.00006417976,0.0001729352,0.002463642,0.002306488],"genre_scores_gemma":[0.6788254,0.000233334,0.3116923,0.0005095484,0.00007903801,0.0002651028,0.0009955146,0.0006172019,0.006782527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003695828,"threshold_uncertainty_score":0.01236373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02555612904498606,"score_gpt":0.3117550062024632,"score_spread":0.2861988771574771,"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."}}