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

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

Code for Simultaneous Edge Alignment and Learning (SEAL)

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

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

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

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

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

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

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

[AAAI 2025] MonoBox: Tightness-free Box-supervised Polyp Segmentation using Monotonicity Constraint

Official codes for ACM CIKM '24 full paper: Tackling Noisy Clients in Federated Learning with End-to-end Label Correction

Official codes for FNBench: Benchmarking Robust Federated Learning against Noisy Labels

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