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

2008NIPS  Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. [Paper][Code]

2009ICML  Supervised learning from multiple experts: whom to trust when everyone lies a bit. [Paper]

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

2013NIPS  Learning with Multiple Labels. [Paper]

2014ML  Learning from multiple annotators with varying expertise. [Paper]

2014  A Comprehensive Introduction to Label Noise. [Paper]

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

2014  Learning from Noisy Labels with Deep Neural Networks. [Paper]

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

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

2015CVPR  Visual recognition by learning from web data: A weakly supervised domain generalization approach. [Paper][Code]

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

2015ICCV  Webly supervised learning of convolutional networks. [Paper][Project Pagee]

2015TPAMI  Classification with noisy labels by importance reweighting. [Paper][Code]

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]

2015TNLS  Rboost: label noiserobust boosting algorithm based on a nonconvex loss function and the numerically stable base learners. [Paper]

2016AAAI  Robust semisupervised learning through label aggregation. [Paper]

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]

2016ICML  Loss factorization, weakly supervised learning and label noise robustness. [Paper]

2016RL  On the convergence of a family of robust losses for stochastic gradient descent. [Paper]

2016NC  Noise detection in the MetaLearning Level. [Paper]

2016ECCV  The Unreasonable Effectiveness of Noisy Data for FineGrained Recognition. [Paper][Project Page]

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

2016ICDM  Learning deep networks from noisy labels with dropout regularization. [Paper][Code]

2016KBS  A robust multiclass AdaBoost algorithm for mislabeled noisy data. [Paper]

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

2017PAKDD  On the Robustness of Decision Tree Learning under Label Noise. [Paper]

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

2017ICLR  Who Said What: Modeling Individual Labelers Improves Classification. [Paper]

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

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

2017CVPR  Lean crowdsourcing: Combining humans and machines in an online system. [Paper][Code]

2017CVPR  Attend in groups: a weaklysupervised deep learning framework for learning from web data. [Paper][Code]

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]

2017NIPS  Active bias: Training more accurate neural networks by emphasizing high variance samples. [Paper][Code]

2017NIPS  Decoupling" when to update" from" how to update". [Paper][Code]

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

2017TNLS  Improving Crowdsourced Label Quality Using Noise Correction. [Paper]

2017ML  Learning to Learn from Weak Supervision by Full Supervision. [Paper][Code]

2017ML  Avoiding your teacher's mistakes: Training neural networks with controlled weak supervision. [Paper]

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

2017  Fidelityweighted learning. [Paper]

2017  SelfErrorCorrecting Convolutional Neural Network for Learning with Noisy Labels. [Paper]

2017  Learning with confident examples: Rank pruning for robust classification with noisy labels. [Paper]

2017  Regularizing neural networks by penalizing confident output distributions. [Paper]

2017  Learning with Auxiliary LessNoisy Labels. [Paper]

2018AAAI  Deep learning from crowds. [Paper]

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

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

2018ICLR  Dimensionality Driven Learning for Noisy Labels. [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]

2018ECCV  Learning with Biased Complementary Labels. [Paper]

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

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

2018WACV  A semisupervised twostage approach to learning from 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]

2018TIP  Deep learning from noisy image labels with quality embedding. [Paper]

2018TNLS  Progressive Stochastic Learning for Noisy Labels. [Paper]

2018  Multiclass Learning with Partially Corrupted Labels. [Paper]

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

2018  Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks. [Paper]

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

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

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][Code]

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]

2019IJCAI  Learning Sound Events from Webly Labeled Data. [Paper] [Code]

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][Code]

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

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]

2019NIPS  Are Anchor Points Really Indispensable in LabelNoise Learning?. [Paper][Code]

2019NIPS  Noisetolerant fair classification. [Paper][Code]

2019NIPS  Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels. [Paper]

2019NIPS  Combinatorial Inference against Label Noise. [Paper][Code]

2019NIPS  L_DMI: A Novel Informationtheoretic Loss Function for Training Deep Nets Robust to Label Noise. [Paper][Code]

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]

2019  Towards Robust Learning with Different Label Noise Distributions. [Paper]

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

2020AAAI  Less Is Better: Unweighted Data Subsampling via Influence Function. [Paper] [Code]

2020AAAI  Label Error Correction and Generation Through Label Relationships. [Paper]

2020AAAI  SelfPaced Robust Learning for Leveraging Clean Labels in Noisy Data. [Paper]

2020AAAI  Coupledview Deep Classifier Learning from Multiple Noisy Annotators. [Paper]

2020AAAI  Partial Multilabel Learning with Noisy Label Identification. [Paper]

2020WACV  A Novel SelfSupervised Relabeling Approach for Training with Noisy Labels. [Paper]

2020WACV  Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision. [Paper]

2020WACV  Learning from Noisy Labels via Discrepant Collaborative Training. [Paper]

2020ICLR  SELF: Learning to Filter Noisy Labels with SelfEnsembling. [Paper]

2020ICLR  DivideMix: Learning with Noisy Labels as Semisupervised Learning. [Paper]

2020ICLR  Can gradient clipping mitigate label noise?. [Paper]

2020ICLR  Curriculum Loss: Robust Learning and Generalization against Label Corruption. [Paper]

2020ICLR  Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee. [Paper]

2020CVPR  Combating noisy labels by agreement: A joint training method with coregularization. [Paper]

2020CVPR  Distilling Effective Supervision From Severe Label Noise. [Paper][Code]

2020CVPR  Learning From Noisy Anchors for OneStage Object Detection. [Paper]

2020CVPR  SelfTraining With Noisy Student Improves ImageNet Classification. [Paper]

2020CVPR  Noise Robust Generative Adversarial Networks. [Paper]

2020CVPR  NoiseAware Fully Webly Supervised Object Detection. [Paper][Code]

2020CVPR  GlobalLocal GCN: LargeScale Label Noise Cleansing for Face Recognition. [Paper]

2020ICML  Learning with Bounded Instanceand Labeldependent Label Noise. [Paper]

2020ICML  LabelNoise Robust Domain Adaptation.

2020ICML  LTF: A Label Transformation Framework for Correcting Label Shift.

2020ICML  Does label smoothing mitigate label noise?. [Paper]

2020ICML  ErrorBounded Correction of Noisy Labels.

2020ICML  Deep kNN for Noisy Labels. [Paper]

2020ICML  Searching to Exploit Memorization Effect in Learning from Noisy Labels. [Paper]

2020ICML  Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels. [Paper]

2020ICML  Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates. [Paper]

2020ICML  Improving Generalization by Controlling LabelNoise Information in Neural Network Weights. [Paper][Code]

2020ICML  Learning Binary Neurons with Noisy Supervision.

2020ICML  S2GA: Robust Deep Learning with Noisy Labels without Early Stopping.

2020ICML  Normalized Loss Functions for Deep Learning with Noisy Labels. [Paper]

2020  MultiClass Classification from NoisySimilarityLabeled Data. [Paper]

2020  NoiseRank: Unsupervised Label Noise Reduction with Dependence Models. [Paper]

2020  Learning Adaptive Loss for Robust Learning with Noisy Labels. [Paper]

2020  Identifying Mislabeled Data using the Area Under the Margin Ranking. [Paper]
Survey

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

2019  Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey. [Paper]

2020  Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. [Paper]
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
 Deep Learning with Label Noise
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