{"id":"W4280641606","doi":"10.24963/ijcai.2022/455","title":"SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Generalization; Code (set theory); Noise (video); Machine learning; Simplicity; Class (philosophy); Artificial neural network; Ensemble learning; Pattern recognition (psychology); Simple (philosophy); Deep neural networks; Mathematics; 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":[0.0008931308,0.0002480588,0.0002284677,0.0002524885,0.0008155801,0.0003907199,0.002020798,0.00005644081,0.0001073936],"category_scores_gemma":[0.0004043165,0.0001935461,0.00008602596,0.0007091445,0.0001107547,0.0006023481,0.0008035938,0.0009127724,0.00006082332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002121462,"about_ca_system_score_gemma":0.0001375885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001409468,"about_ca_topic_score_gemma":0.00002754244,"domain_scores_codex":[0.9973847,0.00005855187,0.0005430999,0.0006398116,0.001075193,0.0002986249],"domain_scores_gemma":[0.9979628,0.0001323526,0.0007550709,0.0002952417,0.0007767741,0.00007772651],"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":[0.0002082378,0.0005076937,0.002134554,0.00004113012,0.00008687747,0.000001956145,0.003265047,0.00282514,0.04519212,0.8782406,0.0007114002,0.06678526],"study_design_scores_gemma":[0.0001627008,0.001085144,0.002045786,0.0001996163,0.00003048082,0.00008361563,0.002230268,0.8442457,0.1171853,0.02492553,0.007288417,0.0005174971],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5056158,0.00007010553,0.2870217,0.07616331,0.01038652,0.002293124,0.00004940194,0.002159916,0.1162402],"genre_scores_gemma":[0.9928484,0.00003224025,0.005175895,0.0002580916,0.00009917315,0.00009347697,0.000008150926,0.00001955865,0.001465001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8533151,"threshold_uncertainty_score":0.7892582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04012080355832404,"score_gpt":0.2659571239602245,"score_spread":0.2258363204019005,"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."}}