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T-Revision
NeurIPS'2019: Are Anchor Points Really Indispensable in Label-Noise Learning?CDR
ICLRβ2021: Robust Early-learning: Hindering the Memorization of Noisy LabelsPart-dependent-label-noise
NeurIPS'2020: Part-dependent Label Noise: Towards Instance-dependent Label NoiseClassification-with-noisy-labels-by-importance-reweighting
TPAMI: Classification with noisy labels by importance reweighting.PICMM
NeurIPS'2022: Pluralistic Image Completion with Gaussian Mixture ModelsHLC
ICCV'2023: Holistic Label Correction for Noisy Multi-Label ClassificationRTM_LNL
Regularly Truncated M-estimators for Learning with Noisy LabelsCoDis
ICCV'2023: Combating Noisy Labels with Sample Selection by Mining High-Discrepancy ExamplesTML-Group
The code that TML group often usesCNLCU
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