LearningwithLabelNoise
A curated list of resources for Learning with Noisy Labels
Papers

2012ICML  Learning to Label Aerial Images from Noisy Data. [Paper]

2013NIPS  Learning with Multiple Labels. [Paper]

2014  A Comprehensive Introduction to Label Noise. [Paper]

2014Survey  Classification in the Presence of Label Noise: a Survey. [Paper]

2015ICLR_W  Training Convolutional Networks with Noisy Labels. [Paper][Code]

2015CVPR  Learning from Massive Noisy Labeled Data for Image Classification. [Paper][Code]

2015CVPR  Training Deep Neural Networks on Noisy Labels with Bootstrapping. [Paper][LossCodeUnofficial1][LossCodeUnofficial2][CodeKeras]

2015NIPS  Learning with Symmetric Label Noise: The Importance of Being Unhinged. [Paper][LossCodeUnofficial]

2015  Making Risk Minimization Tolerant to Label Noise. [Paper]

2015  Learning Discriminative Reconstructions for Unsupervised Outlier Removal. [Paper][Code]

2016ICLR  Auxiliary Image Regularization for Deep CNNs with Noisy Labels. [Paper][Code]

2016CVPR  Seeing through the Human Reporting Bias: Visual Classifiers from Noisy HumanCentric Labels. [Paper][Code]

2016ICASSP  Training deep neuralnetworks based on unreliable labels. [Paper][Poster][CodeUnofficial]

2017AAAI  Robust Loss Functions under Label Noise for Deep Neural Networks. [Paper]

2017ICLR  Training deep neuralnetworks using a noise adaptation layer. [Paper][Code]

2017CVPR  Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach. [Paper] [Code]

2017CVPR  Learning From Noisy LargeScale DatasetsWith Minimal Supervision. [Paper]

2017ICML  Robust Probabilistic Modeling with Bayesian Data Reweighting. [Paper][Code]

2017ICCV  Learning From Noisy Labels With Distillation. [Paper][Code]

2017NIPS  Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks. [Paper]

2017IEEETIFS  A Light CNN for Deep Face Representation with Noisy Labels. [Paper][CodePytorch][CodeKeras][CodeTensorflow]

2017  Deep Learning is Robust to Massive Label Noise. [Paper]

2018ICLR  mixup: Beyond Empirical Risk Minimization. [Paper] [Code]

2018ICLR  Learning From Noisy Singlylabeled Data. [Paper] [Code]

2018ICLR_W  How Do Neural Networks Overcome Label Noise?. [Paper]

2018CVPR  CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise. [Paper] [Code]

2018CVPR  Joint Optimization Framework for Learning with Noisy Labels. [Paper] [Code][CodeUnofficialPytorch]

2018CVPR  Iterative Learning with Openset Noisy Labels. [Paper] [Code]

2018ICML  MentorNet: Learning DataDriven Curriculum for Very Deep Neural Networks on Corrupted Labels. [Paper] [Code]

2018ICML  Learning to Reweight Examples for Robust Deep Learning. [Paper] [Code] [CodeUnofficialPyTorch]

2018ICML  DimensionalityDriven Learning with Noisy Labels. [Paper] [Code]

2018ECCV  CurriculumNet: Weakly Supervised Learning from LargeScale Web Images. [Paper] [Code]

2018ISBI  Training a neural network based on unreliable human annotation of medical images. [Paper]

2018WACV  Iterative Cross Learning on Noisy Labels. [Paper]

2018NIPS  Coteaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels. [Paper] [Code]

2018NIPS  Masking: A New Perspective of Noisy Supervision. [Paper] [Code]

2018NIPS  Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise. [Paper] [Code]

2018NIPS  Robustness of conditional GANs to noisy labels. [Paper] [Code]

2018NIPS  Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. [Paper][LossCodeUnofficial]

2018  Improving MultiPerson Pose Estimation using Label Correction. [Paper]

2019AAAI  Safeguarded Dynamic Label Regression for Generalized Noisy Supervision. [Paper] [Code][Slides][Poster]

2019ICLR_W  SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels.[Paper][Code]

2019CVPR  Learning to Learn from Noisy Labeled Data. [Paper] [Code]

2019CVPR  Learning a Deep ConvNet for Multilabel Classification with Partial Labels. [Paper]

2019CVPR  LabelNoise Robust Generative Adversarial Networks. [Paper] [Code]

2019CVPR  Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion. [Paper]

2019CVPR  Probabilistic Endtoend Noise Correction for Learning with Noisy Labels. [Paper][Code]

2019CVPR  Graph Convolutional Label Noise Cleaner: Train a Plugandplay Action Classifier for Anomaly Detection. [Paper][Code]

2019CVPR  Improving Semantic Segmentation via Video Propagation and Label Relaxation. [Paper][Code]

2019CVPR  Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations. [Paper] [Code][Projectpage]

2019CVPR  NoiseTolerant Paradigm for Training Face Recognition CNNs. [Paper] [Code]

2019CVPR  A Nonlinear, Noiseaware, Quasiclustering Approach to Learning Deep CNNs from Noisy Labels [Paper]

2019ICML  Unsupervised Label Noise Modeling and Loss Correction. [Paper] [Code]

2019ICML  Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels. [Paper] [Code]

2019ICML  How does Disagreement Help Generalization against Label Corruption?. [Paper] [Code]

2019ICML  Using PreTraining Can Improve Model Robustness and Uncertainty [Paper] [Code]

2019ICML  On Symmetric Losses for Learning from Corrupted Labels [Paper] [Poster] [Slides] [Code]

2019ICML  Combating Label Noise in Deep Learning Using Abstention [Paper] [Code]

2019ICASSP  Learning Sound Event Classifiers from Web Audio with Noisy Labels. [Paper] [Code]

2019TGRS  Hyperspectral Image Classification in the Presence of Noisy Labels. [Paper] [Code]

2019ICCV  NLNL: Negative Learning for Noisy Labels. [Paper]

2019ICCV  Symmetric Cross Entropy for Robust Learning With Noisy Labels. [Paper][Code]

2019ICCV  Deep SelfLearning From Noisy Labels. [Paper]

2019ICCV  CoMining: Deep Face Recognition With Noisy Labels.[Paper]

2019ICCV  O2UNet: A Simple Noisy Label Detection Approach for Deep Neural Networks.[Paper]

2019ICCV  Deep SelfLearning From Noisy Labels.[Paper]

2019ICCV_W  Photometric Transformer Networks and Label Adjustment for Breast Density Prediction. [Paper]

2019NIPS  MetaWeightNet: Learning an Explicit Mapping For Sample Weighting.[Paper][Code]

2019TPAMI  Learning from Largescale Noisy Web Data with Ubiquitous Reweighting for Image Classification. [Paper]

2019ISBI  Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification. [Paper]

2019  Curriculum Loss: Robust Learning and Generalization against Label Corruption. [Paper]

2019  ChoiceNet: Robust Learning by Revealing Output Correlations. [Paper]

2019  Robust Learning Under Label Noise With Iterative NoiseFiltering. [Paper]

2019  IMAE for NoiseRobust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters. [Paper][Project page]

2019  Confident Learning: Estimating Uncertainty in Dataset Labels. [Paper] [Code]

2019  Derivative Manipulation for General Example Weighting. [Paper] [Code]

2020AAAI  Reinforcement Learning with Perturbed Rewards. [Paper] [Code]

2020AAAI  Less Is Better: Unweighted Data Subsampling via Influence Function. [Paper] [Code]
Github
 Search 'Noisy Label' Results
 Noisy Labels with Jupyter Notebook
 Noisy Label Neural Network1Tensorflow
 Noisy Label Neural Network2Chainer
 Multitasking Learning With Unreliable Labels
 Kerasnoisylablesfinetune
 Light CNN for Deep Face Recognition, in Tensorflow
 Rankpruning
 Cleanlab: machine learning python package for learning with noisy labels and finding label errors in datasets
Others
 Deep Learning PackageChainer Tutorial
 PaperSemiSupervised Learning Literature Survey
 Cross ValidatedClassification with Noisy Labels
 A little talk on label noise
Acknowledgements
Some of the above contents are borrowed from NoisyLabelsProblemCollection