{"id":"W3177352298","doi":"10.1109/cvpr46437.2021.00228","title":"Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Forgetting; Computer science; Flexibility (engineering); Scalability; Task (project management); Artificial intelligence; Lifelong learning; Filter (signal processing); Machine learning; Computer engineering; Computer vision; Engineering; Mathematics","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":[0.0003440555,0.0001178059,0.0001439531,0.00007174586,0.0004608401,0.0005360865,0.0002174213,0.00005983765,0.000301226],"category_scores_gemma":[0.0003526632,0.0001221774,0.0000818596,0.0003440797,0.0000259374,0.0008092674,0.00008694555,0.0001346932,0.0002006852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003590662,"about_ca_system_score_gemma":0.0001392682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006049252,"about_ca_topic_score_gemma":0.00001191415,"domain_scores_codex":[0.9987447,0.000110015,0.0002365441,0.0004141386,0.0002252103,0.0002693798],"domain_scores_gemma":[0.9990526,0.0001549867,0.00008167508,0.0002598384,0.0003423525,0.0001085909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001815533,0.0002503671,0.001553164,0.00007269743,0.00009366353,0.00009219676,0.002410999,0.01958815,0.4141867,0.3912058,0.04081957,0.1297085],"study_design_scores_gemma":[0.0009169528,0.00006344819,0.0009259533,0.00001087997,0.000008014092,0.00004797439,0.00028485,0.8489697,0.0514603,0.0009257822,0.09610434,0.0002818197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01175584,0.00008428674,0.9747142,0.002580231,0.000400992,0.0001405473,9.063303e-7,0.0003036207,0.01001934],"genre_scores_gemma":[0.5006902,0.00002088121,0.4732075,0.002344517,0.0003761521,0.00005767319,0.0001382126,0.00002105266,0.02314389],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8293815,"threshold_uncertainty_score":0.5169495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03163832748074847,"score_gpt":0.2686653435384802,"score_spread":0.2370270160577317,"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."}}