A curated (most recent) list of resources for Learning with Noisy Labels
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Updated
Feb 29, 2024
A curated (most recent) list of resources for Learning with Noisy Labels
Code for Simultaneous Edge Alignment and Learning (SEAL)
[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
NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"
[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"
Twin Contrastive Learning with Noisy Labels (CVPR 2023)
The official implementation of the ACM MM'21 paper Co-learning: Learning from noisy labels with self-supervision.
MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset
(Pattern Recognition Letters 2023) PyTorch implementation of "Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer"
(L2ID@CVPR2021, TNNLS2022) Boosting Co-teaching with Compression Regularization for Label Noise
Official implementation of our NeurIPS2021 paper: Relative Uncertainty Learning for Facial Expression Recognition
A TensorFlow implementation of "Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels"
Official codes for ACM CIKM '22 full paper: Towards Federated Learning against Noisy Labels via Local Self-Regularization
[ICML'2022] Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020
[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)
A python implementation of tree methods for learning with noisy labels.
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