A curated (most recent) list of resources for Learning with Noisy Labels

Code for Simultaneous Edge Alignment and Learning (SEAL)

The official implementation of the ACM MM'21 paper Co-learning: Learning from noisy labels with self-supervision.

[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"

[ICLR2021] Official Pytorch implementation of "When Optimizing f-Divergence is Robust with Label noise"

Official implementation of the ECCV2022 paper: Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition

Twin Contrastive Learning with Noisy Labels (CVPR 2023)

MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset

Official implementation of our NeurIPS2021 paper: Relative Uncertainty Learning for Facial Expression Recognition

Official codes for ACM CIKM '22 full paper: Towards Federated Learning against Noisy Labels via Local Self-Regularization

(L2ID@CVPR2021, TNNLS2022) Boosting Co-teaching with Compression Regularization for Label Noise

NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"

(Pattern Recognition Letters 2023) PyTorch implementation of "Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer"

Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020

[ICML'2022] Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network

[cvpr2023] implementation of out-of-candidate rectification methods

Code for the paper "A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise" (AAAI 2023)

Code for the KDD-2023 paper: Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler

[PR23] The implementation of the paper ''Learning Visual Question Answering on Controlled Semantic Noisy Labels''