{"id":"W4386243210","doi":"10.1109/crv60082.2023.00023","title":"Class Instance Balanced Learning for Long-Tailed Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Skew; Computer science; Classifier (UML); Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Deep neural networks; Cross entropy; Machine learning; Contextual image classification; Entropy (arrow of time); Training set; Image (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":[0.000454071,0.00009936059,0.0001156763,0.000136564,0.0002439291,0.000180895,0.0004186053,0.00005596662,0.00002976213],"category_scores_gemma":[0.0002238537,0.00009553124,0.00005843384,0.0008036388,0.00002353775,0.0004065877,0.00006937567,0.000137607,0.0004872386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004135423,"about_ca_system_score_gemma":0.00005015637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001444728,"about_ca_topic_score_gemma":0.000007971285,"domain_scores_codex":[0.9988666,0.00006076847,0.0002034074,0.0003586849,0.0002153678,0.0002952424],"domain_scores_gemma":[0.9992097,0.0002343749,0.0001024831,0.0002722871,0.0001071395,0.00007400491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003648262,0.00004618197,0.02185139,0.0000551587,0.00003015256,0.000008797728,0.001733224,0.01573951,0.008467782,0.6734416,0.008716714,0.269873],"study_design_scores_gemma":[0.0005004155,0.00004358662,0.06583235,0.00001105313,0.000001714626,0.000001239303,0.0002345894,0.8668732,0.0002058943,0.001146755,0.06499955,0.0001496391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01052249,0.00001489376,0.9673091,0.002343064,0.0003070837,0.0002259175,3.201179e-7,0.001149095,0.01812809],"genre_scores_gemma":[0.9583269,0.00002046184,0.02185317,0.0004308752,0.00005688418,0.00008446083,0.00002293297,0.00001372505,0.0191906],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9478044,"threshold_uncertainty_score":0.6262629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04861006792535539,"score_gpt":0.2951195273155244,"score_spread":0.2465094593901691,"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."}}