{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004473733,0.0002568181,0.000284269,0.0001383138,0.0002338872,0.0005377032,0.0009377452,0.0002320452,0.00009872669],"category_scores_gemma":[0.0002438268,0.0002655052,0.0001713962,0.00009477812,0.00003773739,0.0002456452,0.0006271134,0.0007467192,0.0003595697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007681033,"about_ca_system_score_gemma":0.0002210776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001824422,"about_ca_topic_score_gemma":0.00001146262,"domain_scores_codex":[0.9980924,0.0001138821,0.0003588308,0.0008630769,0.000296763,0.0002750874],"domain_scores_gemma":[0.9982374,0.0002855801,0.0003150184,0.0007119533,0.0003650821,0.00008501432],"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":[0.00001171618,0.0001834396,0.005972005,0.0002496073,0.0001659988,0.00000301795,0.001193855,0.6842861,0.01738129,0.2228447,0.04409026,0.023618],"study_design_scores_gemma":[0.0003331085,0.00003674567,0.01081261,0.00001308127,0.00001181745,0.000004268898,0.000006563098,0.9775559,0.0009476944,0.002866882,0.007090305,0.0003210051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01746439,0.00002999848,0.9748027,0.002763093,0.0004842934,0.001025077,0.00002608937,0.000509595,0.002894774],"genre_scores_gemma":[0.678853,0.000003375304,0.3160829,0.0003740427,0.0005998739,0.0004235662,0.00123484,0.00002437296,0.002404027],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6613886,"threshold_uncertainty_score":0.9999797,"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."}}