https://github.com/astrogilda/awesome-learning-with-label-noise

A curated list of resources for Learning with Noisy Labels

https://github.com/astrogilda/awesome-learning-with-label-noise

Repository

A curated list of resources for Learning with Noisy Labels

https://github.com/astrogilda/Awesome-Learning-with-Label-Noise/blob/master/

# Learning-with-Label-Noise
A curated list of resources for Learning with Noisy Labels


**Papers**

[-] 2012-ICML - Learning to Label Aerial Images from Noisy Data. [[Paper]](https://www.cs.toronto.edu/~hinton/absps/noisy_maps.pdf)

[-] 2013-NIPS - Learning with Multiple Labels. [[Paper]](https://papers.nips.cc/paper/2234-learning-with-multiple-labels.pdf)

[-] 2013-NIPS - Learning with Noisy Labels. [[Paper]](https://papers.nips.cc/paper/5073-learning-with-noisy-labels.pdf)[[Code]](https://github.com/jamie2017/LearningWithNoisyLabels)

[-] 2014 - A Comprehensive Introduction to Label Noise. [[Paper]](https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-10.pdf)

[-] 2014-Survey - Classification in the Presence of Label Noise: a Survey. [[Paper]](https://pdfs.semanticscholar.org/2c8f/24f859bbbc4193d4d83645ef467bcf25adc2.pdf)

[-] 2015-CVPR - Learning from Massive Noisy Labeled Data for Image Classification. [[Paper]](http://www.ee.cuhk.edu.hk/~xgwang/papers/xiaoXYHWcvpr15.pdf)[[Code]](https://github.com/Cysu/noisy_label)

[-] 2015-CVPR - Training Deep Neural Networks on Noisy Labels with Bootstrapping. [[Paper]](https://arxiv.org/abs/1412.6596)[[Loss-Code-Unofficial-1]](https://github.com/edufonseca/icassp19/blob/master/losses.py)[[Loss-Code-Unofficial-2]](https://github.com/giorgiop/loss-correction/blob/master/loss.py)[[Code-Keras]](https://github.com/dr-darryl-wright/Noisy-Labels-with-Bootstrapping)

[-] 2015-ICLR_W - Training Convolutional Networks with Noisy Labels. [[Paper]](https://arxiv.org/abs/1406.2080)[[Code]](https://github.com/tesatory/convnet-noisy)

[-] 2015-NIPS - Learning with Symmetric Label Noise: The Importance of Being Unhinged. [[Paper]](https://arxiv.org/abs/1505.07634)[[Loss-Code-Unofficial]](https://github.com/giorgiop/loss-correction/blob/master/loss.py)

[-] 2015 - Making Risk Minimization Tolerant to Label Noise. [[Paper]](https://arxiv.org/abs/1403.3610)

[-] 2015 - Learning Discriminative Reconstructions for Unsupervised Outlier Removal. [[Paper]](https://www.ganghua.org/publication/ICCV15b.pdf)[[Code]](https://github.com/ClearMoonlight/SoCG_2019/tree/master/DRAE)

[-] 2016-CVPR - Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels. [[Paper]](https://arxiv.org/abs/1512.06974)[[Code]](https://github.com/imisra/latent-noise-icnm)

[-] 2016-ICLR - Auxiliary Image Regularization for Deep CNNs with Noisy Labels. [[Paper]](https://arxiv.org/abs/1511.07069)[[Code]](https://github.com/azadis/AIR)

[-] 2016-ICASSP - Training deep neural-networks based on unreliable labels. [[Paper]](http://ieeexplore.ieee.org/document/7472164/)[[Poster]](https://alanbekker.files.wordpress.com/2016/03/icassp_poster.pdf)[[Code-Unofficial]](https://github.com/Ryo-Ito/Noisy-Labels-Neural-Network)

[-] 2017-CVPR - Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach. [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/html/Patrini_Making_Deep_Neural_CVPR_2017_paper.html) [[Code]](https://github.com/giorgiop/loss-correction)

[-] 2017-CVPR - Learning From Noisy Large-Scale DatasetsWith Minimal Supervision. [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/html/Veit_Learning_From_Noisy_CVPR_2017_paper.html)

[-] 2017-ICCV - Learning From Noisy Labels With Distillation. [[Paper]](openaccess.thecvf.com/content_iccv_2017/html/Li_Learning_From_Noisy_ICCV_2017_paper.html)[[Code]](https://github.com/raingo/yfcc100m-entity)

[-] 2017-NIPS - Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks. [[Paper]](https://papers.nips.cc/paper/7143-toward-robustness-against-label-noise-in-training-deep-discriminative-neural-networks.pdf)


[-] 2017-ICLR - Training deep neural-networks using a noise adaptation layer. [[Paper]](https://openreview.net/forum?id=H12GRgcxg)[[Code]](https://github.com/udibr/noisy_labels)

[-] 2017-ICML - Robust Probabilistic Modeling with Bayesian Data Reweighting. [[Paper]](https://arxiv.org/abs/1606.03860)[[Code]](https://github.com/yixinwang/robust-rpm-public)

[-] 2017-IEEE-TIFS - A Light CNN for Deep Face Representation with Noisy Labels. [[Paper]](https://arxiv.org/abs/1511.02683)[[Code-Pytorch]](https://github.com/AlfredXiangWu/LightCNN)[[Code-Keras]](https://github.com/AlfredXiangWu/face_verification_experiment)[[Code-Tensorflow]](https://github.com/yxu0611/Tensorflow-implementation-of-LCNN)

[-] 2017-AAAI - Robust Loss Functions under Label Noise for Deep Neural Networks. [[Paper]](https://arxiv.org/abs/1712.09482)

[-] 2017 - Deep Learning is Robust to Massive Label Noise. [[Paper]](https://arxiv.org/abs/1705.10694)

[-] 2018-CVPR - CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise. [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/html/Lee_CleanNet_Transfer_Learning_CVPR_2018_paper.html) [[Code]](https://github.com/kuanghuei/clean-net)

[-] 2018-CVPR - Joint Optimization Framework for Learning with Noisy Labels. [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/html/Tanaka_Joint_Optimization_Framework_CVPR_2018_paper.html) [[Code]](https://github.com/DaikiTanaka-UT/JointOptimization)[[Code-Unofficial-Pytorch]](https://github.com/YU1ut/JointOptimization)

[-] 2018-CVPR - Iterative Learning with Open-set Noisy Labels. [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Iterative_Learning_With_CVPR_2018_paper.html) [[Code]](https://github.com/YisenWang/Iterative_learning)

[-] 2018-ECCV - CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images. [[Paper]](http://openaccess.thecvf.com/content_ECCV_2018/html/Sheng_Guo_CurriculumNet_Learning_from_ECCV_2018_paper.html) [[Code]](https://github.com/guoshengcv/CurriculumNet)

[-] 2018-ISBI - Training a neural network based on unreliable human annotation of medical images. [[Paper]](http://www.eng.biu.ac.il/goldbej/files/2018/01/ISBI_2018_Yair.pdf)

[-] 2018-WACV - Iterative Cross Learning on Noisy Labels. [[Paper]](https://ieeexplore.ieee.org/document/8354192)


[-] 2018-ICLR_W - How Do Neural Networks Overcome Label Noise?. [[Paper]](https://openreview.net/forum?id=ryu4RYJPM)

[-] 2018-NIPS - Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels. [[Paper]](https://papers.nips.cc/paper/8072-co-teaching-robust-training-of-deep-neural-networks-with-extremely-noisy-labels.pdf) [[Code]](https://github.com/bhanML/Co-teaching)

[-] 2018-NIPS - Masking: A New Perspective of Noisy Supervision. [[Paper]](https://papers.nips.cc/paper/7825-masking-a-new-perspective-of-noisy-supervision.pdf) [[Code]](https://github.com/bhanML/Masking)

[-] 2018-NIPS - Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise. [[Paper]](https://papers.nips.cc/paper/8246-using-trusted-data-to-train-deep-networks-on-labels-corrupted-by-severe-noise) [[Code]](https://github.com/mmazeika/glc)

[-] 2018-NIPS - Robustness of conditional GANs to noisy labels. [[Paper]](https://arxiv.org/abs/1811.03205) [[Code]](https://github.com/POLane16/Robust-Conditional-GAN)

[-] 2018-NIPS - Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. [[Paper]](https://papers.nips.cc/paper/8094-generalized-cross-entropy-loss-for-training-deep-neural-networks-with-noisy-labels.pdf)[[Loss-Code-Unofficial]](https://github.com/edufonseca/icassp19/blob/master/losses.py)


[-] 2018-ICLR - mixup: Beyond Empirical Risk Minimization. [[Paper]](https://arxiv.org/abs/1710.09412) [[Code]](https://github.com/facebookresearch/mixup-cifar10)

[-] 2018-ICLR - Learning From Noisy Singly-labeled Data. [[Paper]](https://openreview.net/forum?id=H1sUHgb0Z) [[Code]](https://https://github.com/khetan2/MBEM)



[-] 2018-ICML - MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels. [[Paper]](https://arxiv.org/abs/1712.05055) [[Code]](https://github.com/google/mentornet)

[-] 2018-ICML - Learning to Reweight Examples for Robust Deep Learning. [[Paper]](https://arxiv.org/abs/1803.09050) [[Code]](https://github.com/uber-research/learning-to-reweight-examples) [[Code-Unofficial-PyTorch]](https://github.com/danieltan07/learning-to-reweight-examples)

[-] 2018-ICML - Dimensionality-Driven Learning with Noisy Labels. [[Paper]](https://arxiv.org/abs/1806.02612) [[Code]](https://github.com/xingjunm/dimensionality-driven-learning)


[-] 2018 - Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification. [[Paper]](https://arxiv.org/pdf/1811.00700.pdf)

[-] 2018 - Improving Multi-Person Pose Estimation using Label Correction. [[Paper]](https://arxiv.org/pdf/1811.03331.pdf)

[-] 2019-CVPR - Learning to Learn from Noisy Labeled Data. [[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Learning_to_Learn_From_Noisy_Labeled_Data_CVPR_2019_paper.pdf) [[Code]](https://github.com/LiJunnan1992/MLNT)

[-] 2019-CVPR - Learning a Deep ConvNet for Multi-label Classification with Partial Labels. [[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/html/Durand_Learning_a_Deep_ConvNet_for_Multi-Label_Classification_With_Partial_Labels_CVPR_2019_paper.html)

[-] 2019-CVPR - Label-Noise Robust Generative Adversarial Networks. [[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/html/Kaneko_Label-Noise_Robust_Generative_Adversarial_Networks_CVPR_2019_paper.html) [[Code]](https://github.com/takuhirok/rGAN)

[-] 2019-CVPR - Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion. [[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/html/Tanno_Learning_From_Noisy_Labels_by_Regularized_Estimation_of_Annotator_Confusion_CVPR_2019_paper.html)

[-] 2019-CVPR - Probabilistic End-to-end Noise Correction for Learning with Noisy Labels. [[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/html/Yi_Probabilistic_End-To-End_Noise_Correction_for_Learning_With_Noisy_Labels_CVPR_2019_paper.html)[[Code]](https://github.com/yikun2019/PENCIL)

[-] 2019-CVPR - Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection. [[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/html/Zhong_Graph_Convolutional_Label_Noise_Cleaner_Train_a_Plug-And-Play_Action_Classifier_CVPR_2019_paper.html)[[Code]](https://github.com/jx-zhong-for-academic-purpose/GCN-Anomaly-Detection)

[-] 2019-CVPR - Improving Semantic Segmentation via Video Propagation and Label Relaxation. [[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Improving_Semantic_Segmentation_via_Video_Propagation_and_Label_Relaxation_CVPR_2019_paper.html)[[Code]](https://github.com/NVIDIA/semantic-segmentation)

[-] 2019-CVPR - Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations. [[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/html/Acuna_Devil_Is_in_the_Edges_Learning_Semantic_Boundaries_From_Noisy_CVPR_2019_paper.html) [[Code]](https://github.com/nv-tlabs/STEAL)[[Project-page]](https://nv-tlabs.github.io/STEAL/)

[-] 2019-CVPR - Noise-Tolerant Paradigm for Training Face Recognition CNNs. [[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/html/Hu_Noise-Tolerant_Paradigm_for_Training_Face_Recognition_CNNs_CVPR_2019_paper.html) [[Code]](https://github.com/huangyangyu/NoiseFace)

[-] 2019-ICML - Unsupervised Label Noise Modeling and Loss Correction. [[Paper]](https://arxiv.org/abs/1904.11238) [[Code]](https://github.com/PaulAlbert31/LabelNoiseCorrection)

[-] 2019-ICML - Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels. [[Paper]](http://proceedings.mlr.press/v97/chen19g.html) [[Code]](https://github.com/chenpf1025/noisy_label_understanding_utilizing)

[-] 2019-ICML - How does Disagreement Help Generalization against Label Corruption?. [[Paper]](https://arxiv.org/abs/1901.04215) [[Code]](https://github.com/bhanML/coteaching_plus)

[-] 2019-ICML - Using Pre-Training Can Improve Model Robustness and Uncertainty [[Paper]](https://arxiv.org/abs/1901.09960) [[Code]](https://github.com/hendrycks/pre-training)

[-] 2019-ICML - On Symmetric Losses for Learning from Corrupted Labels [[Paper]](https://arxiv.org/abs/1901.09314) [[Poster]](https://nolfwin.github.io/assets/poster/ICML2019_Symloss_poster.pdf) [[Slides]](https://nolfwin.github.io/assets/slides/ICML2019_Symloss_slides.pdf) [[Code]](https://github.com/nolfwin/symloss-ber-auc)

[-] 2019-AAAI - Safeguarded Dynamic Label Regression for Generalized Noisy Supervision. [[Paper]](https://sunarker.github.io/temp/AAAI2019_Dynamic_Label_Regression_for_Noisy_Supervision.pdf) [[Code]](https://github.com/Sunarker/Safeguarded-Dynamic-Label-Regression-for-Noisy-Supervision)[[Slides]](https://sunarker.github.io/temp/AAAI2019_Presentation.pdf)[[Poster]](https://sunarker.github.io/temp/AAAI2019_Poster.pdf)

[-] 2019-ICASSP - Learning Sound Event Classifiers from Web Audio with Noisy Labels. [[Paper]](https://arxiv.org/abs/1901.01189) [[Code]](https://github.com/edufonseca/icassp19)

[-] 2019-TGRS - Hyperspectral Image Classification in the Presence of Noisy Labels. [[Paper]](https://arxiv.org/abs/1809.04212) [[Code]](https://github.com/junjun-jiang/RLPA)

[-] 2019-ICCV - NLNL: Negative Learning for Noisy Labels. [[Paper]](https://arxiv.org/abs/1908.07387)

[-] 2019-ICCV - Symmetric Cross Entropy for Robust Learning With Noisy Labels. [[Paper]](https://arxiv.org/abs/1908.06112)[[Code]](https://github.com/YisenWang/symmetric_cross_entropy_for_noisy_labels)

[-] 2019-ICCV - Deep Self-Learning From Noisy Labels. [[Paper]](https://arxiv.org/abs/1908.02160)

[-] 2019-ICCV - Co-Mining: Deep Face Recognition With Noisy Labels.[[Paper]](http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Co-Mining_Deep_Face_Recognition_With_Noisy_Labels_ICCV_2019_paper.html)

[-] 2019-ICCV - O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks.[[Paper]](http://openaccess.thecvf.com/content_ICCV_2019/html/Huang_O2U-Net_A_Simple_Noisy_Label_Detection_Approach_for_Deep_Neural_ICCV_2019_paper.html)

[-] 2019-ICCV - Deep Self-Learning From Noisy Labels.[[Paper]](http://openaccess.thecvf.com/content_ICCV_2019/html/Han_Deep_Self-Learning_From_Noisy_Labels_ICCV_2019_paper.html)

[-] 2019 - Curriculum Loss: Robust Learning and Generalization against Label Corruption. [[Paper]](https://arxiv.org/abs/1905.10045)

[-] 2019 - ChoiceNet: Robust Learning by Revealing Output Correlations. [[Paper]](https://openreview.net/forum?id=S1MQ6jCcK7)

[-] 2019 - Photometric Transformer Networks and Label Adjustment for Breast Density Prediction. [[Paper]](https://arxiv.org/abs/1905.02906)

[-] 2019 - Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification. [[Paper]](https://arxiv.org/abs/1901.07759)

[-] 2019 - Improving MAE against CCE under Label Noise. [[Paper]](https://arxiv.org/pdf/1903.12141v3.pdf)[[Project page]](https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE)

[-] 2019 - Robust Learning Under Label Noise With Iterative Noise-Filtering. [[Paper]](https://arxiv.org/abs/1906.00216)

[-] 2019 - Confident Learning: Estimating Uncertainty in Dataset Labels. [[Paper]](https://arxiv.org/abs/1911.00068) [[Code]](https://github.com/cgnorthcutt/cleanlab)




**Github**
- [Search 'Noisy Label' Results](https://github.com/search?p=1&q=noisy+label&type=Repositories&utf8=%E2%9C%93)
- [Noisy Labels with Jupyter  Notebook](https://github.com/udibr/noisy_labels)
- [Noisy Label Neural Network1-Tensorflow](https://github.com/EstherMaria/NoisyLabelNeuralNetwork)
- [Noisy Label Neural Network2-Chainer](https://github.com/Ryo-Ito/Noisy-Labels-Neural-Network)
- [Multi-tasking Learning With Unreliable Labels](https://github.com/debjitpaul/Multi-tasking_Learning_With_Unreliable_Labels)
- [Keras-noisy-lables-finetune](https://github.com/nagash91/keras-noisy-lables-finetune) 
- [Light CNN for Deep Face Recognition, in Tensorflow](https://github.com/yxu0611/Tensorflow-implementation-of-LCNN)
- [Rankpruning](https://github.com/cgnorthcutt/rankpruning)
- [Cleanlab: machine learning python package for learning with noisy labels and finding label errors in datasets](https://github.com/cgnorthcutt/cleanlab)

**Others**
- [Deep Learning Package-Chainer Tutorial](https://docs.chainer.org/en/stable/tutorial/index.html)
- [Paper-Semi-Supervised Learning Literature Survey](http://pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf)
- [Cross Validated-Classification with Noisy Labels](https://stats.stackexchange.com/questions/218656/classification-with-noisy-labels)
- [A little talk on label noise](http://knowdive.disi.unitn.it/2018/09/a-little-talk-on-label-noise/)


## Acknowledgements
Some of the above contents are borrowed from [Noisy-Labels-Problem-Collection](https://github.com/GuokaiLiu/Noisy-Labels-Problem-Collection)

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