ForrestPi / Awesome-Learning-with-Label-Noise

A curated list of resources for Learning with Noisy Labels

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A curated list of resources for Learning with Noisy Labels


  • 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 - A Comprehensive Introduction to Label Noise. [Paper]

  • 2014-Survey - Classification in the Presence of Label Noise: a Survey. [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 - Training Deep Neural Networks on Noisy Labels with Bootstrapping. [Paper][Loss-Code-Unofficial-1][Loss-Code-Unofficial-2][Code-Keras]

  • 2015-NIPS - Learning with Symmetric Label Noise: The Importance of Being Unhinged. [Paper][Loss-Code-Unofficial]

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

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

  • 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-ICASSP - Training deep neural-networks based on unreliable labels. [Paper][Poster][Code-Unofficial]

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

  • 2017-ICLR - Training deep neural-networks using a noise adaptation layer. [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 DatasetsWith Minimal Supervision. [Paper]

  • 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-IEEE-TIFS - A Light CNN for Deep Face Representation with Noisy Labels. [Paper][Code-Pytorch][Code-Keras][Code-Tensorflow]

  • 2017 - Deep Learning is Robust to Massive Label Noise. [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-ISBI - Training a neural network based on unreliable human annotation of medical images. [Paper]

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

  • 2018-NIPS - Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels. [Paper] [Code]

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

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

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

  • 2018-NIPS - Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. [Paper][Loss-Code-Unofficial]

  • 2018 - Improving Multi-Person Pose Estimation using Label Correction. [Paper]

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

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

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

  • 2019-CVPR - Learning a Deep ConvNet for Multi-label Classification with Partial Labels. [Paper]

  • 2019-CVPR - Label-Noise Robust Generative Adversarial Networks. [Paper] [Code]

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

  • 2019-CVPR - Probabilistic End-to-end Noise Correction for Learning with Noisy Labels. [Paper][Code]

  • 2019-CVPR - Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection. [Paper][Code]

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

  • 2019-CVPR - Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations. [Paper] [Code][Project-page]

  • 2019-CVPR - Noise-Tolerant Paradigm for Training Face Recognition CNNs. [Paper] [Code]

  • 2019-CVPR - A Nonlinear, Noise-aware, Quasi-clustering Approach to Learning Deep CNNs from Noisy Labels [Paper]

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

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

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

  • 2019-ICML - Using Pre-Training Can Improve Model Robustness and Uncertainty [Paper] [Code]

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

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

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

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

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

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

  • 2019-ICCV - Deep Self-Learning From Noisy Labels. [Paper]

  • 2019-ICCV - Co-Mining: Deep Face Recognition With Noisy Labels.[Paper]

  • 2019-ICCV - O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks.[Paper]

  • 2019-ICCV - Deep Self-Learning From Noisy Labels.[Paper]

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

  • 2019-NIPS - Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting.[Paper][Code]

  • 2019-TPAMI - Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification. [Paper]

  • 2019-ISBI - 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 Noise-Filtering. [Paper]

  • 2019 - IMAE for Noise-Robust 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]

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

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




Some of the above contents are borrowed from Noisy-Labels-Problem-Collection

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A curated list of resources for Learning with Noisy Labels