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A curated (most recent) list of resources for Learning with Noisy Labels
The official implementation of the ACM MM'2021 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
MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset
Official implementation of our NeurIPS2021 paper: Relative Uncertainty Learning for Facial Expression Recognition
(L2ID@CVPR2021, TNNLS2022) Boosting Co-teaching with Compression Regularization for Label Noise
Official codes for CIKM '22 full paper: Towards Federated Learning against Noisy Labels via Local Self-Regularization
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 KDD-2023 paper: Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
A TensorFlow implementation of "Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels"
A python implementation of tree methods for learning with noisy labels.
A curated list of awesome Weak-Supervision-Sequence-Labeling (WSSL) papers, methods & resources.
Official Pytorch Implementation of CrossSplit (ICML 2023)
Training a deep learning model based on noisy labels from a rule based algorithm.
This is the official code for our submission in the expression track of ABAW 2023 competition as a part of CVPR 2023.