Awesome-Learning-with-Label-Noise

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A curated list of resources for Learning with Noisy Labels

Learning-with-Label-Noise

Papers & Code

2008-NIPS - Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. [Paper] [Code]
2009-ICML - Supervised learning from multiple experts: whom to trust when everyone lies a bit. [Paper]
2011-NIPS - Bayesian Bias Mitigation for Crowdsourcing. [Paper]
2012-ICML - Learning to Label Aerial Images from Noisy Data. [Paper]
2013-NIPS - Learning with Multiple Labels. [Paper]
2013-NIPS - Learning with Noisy Labels. [Paper] [Code]
2014-ML - Learning from multiple annotators with varying expertise. [Paper]
2014 - A Comprehensive Introduction to Label Noise. [Paper]
2014 - Learning from Noisy Labels with Deep Neural Networks. [Paper]
2015-ICLR_W - Training Convolutional Networks with Noisy Labels. [Paper] [Code]
2015-CVPR - Learning from Massive Noisy Labeled Data for Image Classification. [Paper] [Code]
2015-CVPR - Visual recognition by learning from web data: A weakly supervised domain generalization approach. [Paper] [Code]
2015-CVPR - Training Deep Neural Networks on Noisy Labels with Bootstrapping. [Paper] [Loss-Code-Unofficial-1] [Loss-Code-Unofficial-2] [Code-Keras]
2015-ICCV - Webly supervised learning of convolutional networks. [Paper] [Project Pagee]
2015-TPAMI - Classification with noisy labels by importance reweighting. [Paper] [Code]
2015-NIPS - Learning with Symmetric Label Noise: The Importance of Being Unhinged. [Paper] [Loss-Code-Unofficial]
2015-Arxiv - Making Risk Minimization Tolerant to Label Noise. [Paper]
2015 - Learning Discriminative Reconstructions for Unsupervised Outlier Removal. [Paper] [Code]
2015-TNLS - Rboost: label noise-robust boosting algorithm based on a nonconvex loss function and the numerically stable base learners. [Paper]
2016-AAAI - Robust semi-supervised learning through label aggregation. [Paper]
2016-ICLR - Auxiliary Image Regularization for Deep CNNs with Noisy Labels. [Paper] [Code]
2016-CVPR - Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels. [Paper] [Code]
2016-ICML - Loss factorization, weakly supervised learning and label noise robustness. [Paper]
2016-RL - On the convergence of a family of robust losses for stochastic gradient descent. [Paper]
2016-NC - Noise detection in the Meta-Learning Level. [Paper] [Additional information]
2016-ECCV - The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition. [Paper] [Project Page]
2016-ICASSP - Training deep neural-networks based on unreliable labels. [Paper] [Poster] [Code-Unofficial]
2016-ICDM - Learning deep networks from noisy labels with dropout regularization. [Paper] [Code]
2016-KBS - A robust multi-class AdaBoost algorithm for mislabeled noisy data. [Paper]
2017-AAAI - Robust Loss Functions under Label Noise for Deep Neural Networks. [Paper]
2017-PAKDD - On the Robustness of Decision Tree Learning under Label Noise. [Paper]
2017-ICLR - Training deep neural-networks using a noise adaptation layer. [Paper] [Code]
2017-ICLR - Who Said What: Modeling Individual Labelers Improves Classification. [Paper] [Code]
2017-CVPR - Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach. [Paper] [Code]
2017-CVPR - Learning From Noisy Large-Scale Datasets With Minimal Supervision. [Paper]
2017-CVPR - Lean crowdsourcing: Combining humans and machines in an online system. [Paper] [Code]
2017-CVPR - Attend in groups: a weakly-supervised deep learning framework for learning from web data. [Paper] [Code]
2017-ICML - Robust Probabilistic Modeling with Bayesian Data Reweighting. [Paper] [Code]
2017-ICCV - Learning From Noisy Labels With Distillation. [Paper] [Code]
2017-NIPS - Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks. [Paper]
2017-NIPS - Active bias: Training more accurate neural networks by emphasizing high variance samples. [Paper] [Code]
2017-NIPS - Decoupling" when to update" from" how to update". [Paper] [Code]
2017-IEEE-TIFS - A Light CNN for Deep Face Representation with Noisy Labels. [Paper] [Code-Pytorch] [Code-Keras] [Code-Tensorflow]
2017-TNLS - Improving Crowdsourced Label Quality Using Noise Correction. [Paper]
2017-ML - Learning to Learn from Weak Supervision by Full Supervision. [Paper] [Code]
2017-ML - Avoiding your teacher's mistakes: Training neural networks with controlled weak supervision. [Paper]
2017-Arxiv - Deep Learning is Robust to Massive Label Noise. [Paper]
2017-Arxiv - Fidelity-weighted learning. [Paper]
2017 - Self-Error-Correcting Convolutional Neural Network for Learning with Noisy Labels. [Paper]
2017-Arxiv - Learning with confident examples: Rank pruning for robust classification with noisy labels. [Paper] [Code]
2017-Arxiv - Regularizing neural networks by penalizing confident output distributions. [Paper]
2017 - Learning with Auxiliary Less-Noisy Labels. [Paper]
2018-AAAI - Deep learning from crowds. [Paper]
2018-ICLR - mixup: Beyond Empirical Risk Minimization. [Paper] [Code]
2018-ICLR - Learning From Noisy Singly-labeled Data. [Paper] [Code]
2018-ICLR_W - How Do Neural Networks Overcome Label Noise?. [Paper]
2018-CVPR - CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise. [Paper] [Code]
2018-CVPR - Joint Optimization Framework for Learning with Noisy Labels. [Paper] [Code] [Code-Unofficial-Pytorch]
2018-CVPR - Iterative Learning with Open-set Noisy Labels. [Paper] [Code]
2018-ICML - MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels. [Paper] [Code]
2018-ICML - Learning to Reweight Examples for Robust Deep Learning. [Paper] [Code] [Code-Unofficial-PyTorch]
2018-ICML - Dimensionality-Driven Learning with Noisy Labels. [Paper] [Code]
2018-ECCV - CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images. [Paper] [Code]
2018-ECCV - Deep Bilevel Learning. [Paper] [Code]
2018-ECCV - Learning with Biased Complementary Labels. [Paper] [Code]
2018-ISBI - Training a neural network based on unreliable human annotation of medical images. [Paper]
2018-WACV - Iterative Cross Learning on Noisy Labels. [Paper]
2018-WACV - A semi-supervised two-stage approach to learning from noisy labels.