[CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".
[CVPR'22] Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning"
Tilted Empirical Risk Minimization (ICLR '21)
Offical pytorch implementation of proposed NRGNN and Compared Methods in "NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs" (KDD 2021).
Label-Noise Learning with Intrinsically Long-Tailed Data（ICCV2023）
Extra bits of unsanitized code for plotting, training, etc. related to our CVPR 2021 paper "Augmentation Strategies for Learning with Noisy Labels".
Label Noise-Robust Learning for Microseismic Arrival Time Picking
PyTorch Implementation of Robust Cross Entropy Loss (Loss Correction for Label Noise)
Implementations of different loss-correction techniques to help deep models learn under class-conditional label noise.
Semester project on the impact of label noise on deep learning optimization
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