{"id":"W4317036100","doi":"10.1145/3576045","title":"Distilled Meta-learning for Multi-Class Incremental Learning","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Forgetting; Meta learning (computer science); Computer science; Artificial intelligence; Machine learning; Incremental learning; Benchmark (surveying); Task (project management); Class (philosophy); Active learning (machine learning); 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":[0.002605806,0.001388397,0.001984918,0.001444325,0.000572102,0.001317959,0.004487699,0.001637295,0.002124636],"category_scores_gemma":[0.007427581,0.0007544204,0.001283782,0.001308069,0.001212639,0.003787432,0.002398374,0.003125891,0.0007124307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001103794,"about_ca_system_score_gemma":0.001331888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002467308,"about_ca_topic_score_gemma":0.00411598,"domain_scores_codex":[0.9989624,0.0003063995,0.00007667443,0.0003124946,0.0002328219,0.0001091714],"domain_scores_gemma":[0.9970074,0.00159403,0.0002046073,0.0006080157,0.0004420749,0.0001437455],"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":[0.0002176168,0.0003420082,0.002092325,0.000376887,0.000291092,0.0001935147,0.0002527771,0.5285652,0.00508192,0.02126365,0.005303134,0.43602],"study_design_scores_gemma":[0.00001433573,0.00004811932,0.00009178816,0.00001508073,0.00002729921,0.00002990564,0.00001016243,0.9850825,0.001180997,0.01269556,0.0007931866,0.00001113159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01158082,0.001129869,0.9847227,0.0002162916,0.00007725049,0.00008450294,0.00008438134,0.001227113,0.0008771343],"genre_scores_gemma":[0.6326296,0.0008677507,0.3618891,0.0005743024,0.0002426257,0.0004579651,0.0006172657,0.0002860059,0.002435565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004487699,"threshold_uncertainty_score":0.01378101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1055085410575087,"score_gpt":0.3442216972377931,"score_spread":0.2387131561802844,"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."}}