[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)
Semester project on the impact of label noise on deep learning optimization
Implementations of different loss-correction techniques to help deep models learn under class-conditional label noise.
Add a description, image, and links to the label-noise-robustness topic page so that developers can more easily learn about it.
To associate your repository with the label-noise-robustness topic, visit your repo's landing page and select "manage topics."