Learning-with-Noisy-Labels

A curated list of most recent papers & codes in Learning with Noisy Labels

Some recent works about group-distributional robustness, label distribution shifts, are also included.

Public Software

Docta-AI: An advanced data-centric AI platform that detects and rectifies issues in any data format (i.e., label error detection). [Website]

Competition

1st Learning and Mining with Noisy Labels Challenge (IJCAI 2022)[website]

Content

• Benchmarks & Leaderboard
• Papers & Code in 2023
  • ICLR 2023
• Papers & Code in 2022
  • NeurIPS 2022
  • ECCV 2022
  • ICML 2022
  • CVPR 2022
  • ICLR 2022
  • AISTATS 2022
  • Other Conferences 2022
  • ArXiv 2022
• Papers & Code in 2021
  • NeurIPS 2021
  • IJCAI 2021
  • ICML 2021
  • ICLR 2021
  • CVPR 2021
  • AISTATS 2021
  • AAAI 2021
  • Other Conferences 2021
  • ArXiv 2021
• Papers & Code in 2020
  • ICML 2020
  • ICLR 2020
  • Nips 2020
  • AAAI 2020
  • CVPR 2020
  • ECCV 2020
  • ArXiv 2020

Benchmarks & Leaderboard

Real-world noisy-label bechmarks:

Simulation of label noise: An Instance-Dependent Simulation Framework for Learning with Label Noise. [Paper]

This repo focus on papers after 2019, for previous works, please refer to (https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise).

Papers & Code in 2023

ICLR 2023

• [UCSC REAL Lab] Distributionally Robust Post-hoc Classifiers under Prior Shifts. [Paper][Code]
• [UCSC REAL Lab] Mitigating Memorization of Noisy Labels via Regularization between Representations. [Paper & Code]
• On the Edge of Benign Overfitting: Label Noise and Overparameterization Level. [Paper & Code]
• Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization. [Paper & Code]
• CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos. [Paper & Code]
• Learning to Segment from Noisy Annotations: A Spatial Correction Approach. [Paper & Code]
• Mutual Partial Label Learning with Competitive Label Noise. [Paper & Code]
• Memorization-Dilation: Modeling Neural Collapse Under Noise. [Paper & Code]
• Leveraging Unlabeled Data to Track Memorization . [Paper & Code]
• Quantifying and Mitigating the Impact of Label Errors on Model Disparity Metrics. [Paper & Code]
• When Source-Free Domain Adaptation Meets Learning with Noisy Labels. [Paper & Code]
• A law of adversarial risk, interpolation, and label noise. [Paper & Code]
• SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning. [Paper & Code]
• CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos. [Paper & Code]
• Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision?. [Paper & Code]
• Label Propagation with Weak Supervision . [Paper & Code]
• Mitigating Dataset Bias by Using Per-Sample Gradient. [Paper & Code]
• MCAL: Minimum Cost Human-Machine Active Labeling. [Paper & Code]
• Avoiding spurious correlations via logit correction. [Paper & Code]
• Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts . [Paper & Code]
• Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. [Paper & Code]
• Towards Lightweight, Model-Agnostic and Diversity-Aware Active Anomaly Detection. [Paper & Code]
• Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic. [Paper & Code]
• Towards Addressing Label Skews in One-Shot Federated Learning. [Paper & Code]
• Instance-wise Batch Label Restoration via Gradients in Federated Learning. [Paper & Code]
• That Label's got Style: Handling Label Style Bias for Uncertain Image Segmentation. [Paper & Code]
• Learning Hyper Label Model for Programmatic Weak Supervision. [Paper & Code]
• Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. [Paper & Code]

Papers & Code in 2022

NeurIPS 2022

• Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning . [Paper][Code]
• Learning from Label Proportions by Learning with Label Noise. [Paper & Code]
• Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization. [Paper & Code]
• On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice. [Paper & Code]
• Robustness to Label Noise Depends on the Shape of the Noise Distribution. [Paper & Code]
• Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting. [Paper & Code]
• SoftPatch: Unsupervised Anomaly Detection with Noisy Data. [Paper & Code]

ECCV 2022

• Neighborhood Collective Estimation for Noisy Label Identification and Correction. [Paper][Code]
• Teaching with Soft Label Smoothing for Mitigating Noisy Labels in Facial Expressions. [Paper][Code]
• Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion. [Paper][Code]
• Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition. [Paper][Code]
• Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels. [Paper][Code]
• Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection. [Paper][Code]
• Identifying Hard Noise in Long-Tailed Sample Distribution. [Paper][Code]
• Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization. [Paper]
• BoundaryFace: A mining framework with noise label self-correction for Face Recognition. [Paper][Code]
• Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets. [Paper][Code]
• WeLSA: Learning To Predict 6D Pose From Weakly Labeled Data Using Shape Alignment. [Paper]
• Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing. [Paper][Code]
• PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection. [Paper][Code]
• Active label correction using robust parameter update and entropy propagation. [Paper]
• A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering. [Paper][Code]

ICML 2022

• [UCSC REAL Lab] To Smooth or Not? When Label Smoothing Meets Noisy Labels. [Paper][Code]
• [UCSC REAL Lab] Detecting Corrupted Labels Without Training a Model to Predict. [Paper][Code]
• [UCSC REAL Lab] Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features. [Paper]
• From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model [Paper][Code]
• Robust Training under Label Noise by Over-parameterization. [Paper][Code]
• Estimating Instance-dependent Label-noise Transition Matrix using DNNs. [Paper]
• Transfer and Marginalize: Explaining Away Label Noise with Privileged Information. [Paper]
• Guaranteed Robust Deep Learning against Extreme Label Noise using Self-supervised Learning.
• Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile. [Paper][Code]
• Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent. [Paper]
• Guaranteed Robust Deep Learning against Extreme Label Noise using Self-supervised Learning.

CVPR 2022

• Selective-Supervised Contrastive Learning with Noisy Labels. [Paper][Code]
• Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels. [Paper][Code]
• Large-Scale Pre-training for Person Re-identification with Noisy Labels. [Paper][Code]
• Adaptive Early-Learning Correction for Segmentation from Noisy Annotations. [Paper][Code]

ICLR 2022

• [UCSC REAL Lab] Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. [Paper][Code]
• Resolving Training Biases via Influence-based Data Relabeling. [Paper and Code]
• Contrastive Label Disambiguation for Partial Label Learning. [Paper and Code]
• Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. [Paper and Code]
• An Information Fusion Approach to Learning with Instance-Dependent Label Noise. [Paper and Code]
• Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. [Paper and Code]

AISTATS 2022

• Robustness and reliability when training with noisy labels. [Paper]
• A Spectral Perspective of DNN Robustness to Label Noise.
• Hardness of Learning a Single Neuron with Adversarial Label Noise.
• Learning from Multiple Noisy Partial Labelers. [Paper]

Other Conferences 2022

ArXiv 2022

• [UCSC REAL Lab] To Aggregate or Not? Learning with Separate Noisy Labels. [Paper]
• [UCSC REAL Lab] Identifiability of Label Noise Transition Matrix. [Paper]
• Constrained Instance and Class Reweighting for Robust Learning under Label Noise. [Paper]
• AUGLOSS: A Learning Methodology for Real-World Dataset Corruption. [Paper]
• Do We Need to Penalize Variance of Losses for Learning with Label Noise?. [Paper]
• Robust Training under Label Noise by Over-parameterization. [Paper][Code]
• On Learning Contrastive Representations for Learning with Noisy Labels. [Paper]
• Learning from Label Proportions by Learning with Label Noise. [Paper]
• Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. [Paper]
• Synergistic Network Learning and Label Correction for Noise-robust Image Classification. [Paper]
• Transfer and Marginalize: Explaining Away Label Noise with Privileged Information. [Paper]
• Convolutional Network Fabric Pruning With Label Noise. [Paper]
• Learning to Bootstrap for Combating Label Noise. [Paper]
• Learning with Neighbor Consistency for Noisy Labels. [Paper]
• Investigating Why Contrastive Learning Benefits Robustness Against Label Noise. [Paper]
• PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels. [Paper]
• GMM Discriminant Analysis with Noisy Label for Each Class. [Paper]
• Learning with Label Noise for Image Retrieval by Selecting Interactions. [Paper]
• Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise. [Paper]

Papers & Code in 2021

NeurIPS 2021

Conference date: Dec, 6th -- Dec, 14th

• [UCSC REAL Lab] Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial. [Paper][Code]
• Open-set Label Noise Can Improve Robustness Against Inherent Label Noise. [Paper][Code]
• Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels. [Paper][Code]
• Understanding and Improving Early Stopping for Learning with Noisy Labels. [Paper][Code]
• How does a Neural Network's Architecture Impact its Robustness to Noisy Labels? [Paper][Code]
• FINE Samples for Learning with Noisy Labels. [Paper][Code]
• Label Noise SGD Provably Prefers Flat Global Minimizers. [Paper][Code]
• Improved Regularization and Robustness for Fine-tuning in Neural Networks. [Paper][Code]
• Instance-dependent Label-noise Learning under a Structural Causal Model. [Paper]
• Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification. [Paper]
• DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples. [Paper]
• Corruption Robust Active Learning. [Paper]

IJCAI 2021

• Learning Implicitly with Noisy Data in Linear Arithmetic. [Paper]
• Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion. [Paper]
• Modeling Noisy Hierarchical Types in Fine-Grained Entity Typing: A Content-Based Weighting Approach. [Paper]
• Multi-level Generative Models for Partial Label Learning with Non-random Label Noise. [Paper]

ICML 2021

Conference date: Jul 18, 2021 -- Jul 24, 2021

• [UCSC REAL Lab] The importance of understanding instance-level noisy labels. [Paper]
• [UCSC REAL Lab] Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels. [Paper][Code]
• Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision. [Paper][Code]
• Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. [Paper][Code]
• Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. [Paper]
• Provably End-to-end Label-noise Learning without Anchor Points. [Paper]
• Asymmetric Loss Functions for Learning with Noisy Labels. [Paper][Code]
• Confidence Scores Make Instance-dependent Label-noise Learning Possible. [Paper]
• Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. [Paper]
• Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data. [Paper]
• Learning from Noisy Labels with No Change to the Training Process. [Paper]

ICLR 2021

• [UCSC REAL Lab] When Optimizing f-Divergence is Robust with Label Noise. [Paper][Code]
• [UCSC REAL Lab] Learning with Instance-Dependent Label Noise: A Sample Sieve Approach. [Paper][Code]
• Noise against noise: stochastic label noise helps combat inherent label noise. [Paper][Code]
• Learning with Feature-Dependent Label Noise: A Progressive Approach. [Paper][Code]
• Robust early-learning: Hindering the memorization of noisy labels. [Paper][Code]
• MoPro: Webly Supervised Learning with Momentum Prototypes. [Paper] [Code]
• Robust Curriculum Learning: from clean label detection to noisy label self-correction. [Paper]
• How Does Mixup Help With Robustness and Generalization? [Paper]
• Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data. [Paper]

CVPR 2021

Conference date: Jun 19, 2021 -- Jun 25, 2021

• [UCSC REAL Lab] A Second-Order Approach to Learning with Instance-Dependent Label Noise. [Paper][Code]
• Improving Unsupervised Image Clustering With Robust Learning. [Paper]
• Multi-Objective Interpolation Training for Robustness to Label Noise. [Paper][Code]
• Noise-resistant Deep Metric Learning with Ranking-based Instance Selection. [Paper][Code]
• Augmentation Strategies for Learning with Noisy Labels. [Paper][Code]
• Jo-SRC: A Contrastive Approach for Combating Noisy Labels. [Paper][Code]
• Multi-Objective Interpolation Training for Robustness to Label Noise. [Paper][Code]
• Partially View-aligned Representation Learning with Noise-robust Contrastive Loss. [Paper][Code]
• Correlated Input-Dependent Label Noise in Large-Scale Image Classification. [Paper]
• DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature Distributions.[Paper]
• Faster Meta Update Strategy for Noise-Robust Deep Learning. [Paper][Code]
• DualGraph: A graph-based method for reasoning about label noise. [Paper]
• Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation. [Paper]
• Joint Negative and Positive Learning for Noisy Labels. [Paper]
• Faster Meta Update Strategy for Noise-Robust Deep Learning. [Paper]
• AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation. [Paper][Code]
• Meta Pseudo Labels. [Paper][Code]
• All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training. [Paper][Code]
• SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification. [Paper][Code]

AISTATS 2021

Conference date: Apr 13, 2021 -- Apr 15, 2021

• Collaborative Classification from Noisy Labels. [Paper]
• Linear Models are Robust Optimal Under Strategic Behavior. [Paper]

AAAI 2021

• Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise. [Paper][Code]
• Learning to Purify Noisy Labels via Meta Soft Label Corrector. [Paper][Code]
• Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels. [Paper][Code]
• Learning from Noisy Labels with Complementary Loss Functions. [Paper][Code]
• Analysing the Noise Model Error for Realistic Noisy Label Data. [Paper][Code]
• Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. [Paper]
• Learning with Group Noise. [Paper]
• Meta Label Correction for Noisy Label Learning. [Paper]

Other Conferences 2021

• (ICCV 2021) Learning with Noisy Labels for Robust Point Cloud Segmentation. [Paper][Code]
• (ICCV 2021) Learning with Noisy Labels via Sparse Regularization. [Paper]
• (WACV 2022) Towards a Robust Differentiable Architecture Search under Label Noise. [Paper]
• (WACV 2022) Addressing out-of-distribution label noise in webly-labelled data. [Paper][Code]
• (BMVC 2021) PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels. [Paper][Code]
• (IJCAI2021 Workshop) An Ensemble Noise-Robust K-fold Cross-Validation Selection Method for Noisy Labels. [Paper]

ArXiv 2021

• [UCSC REAL Lab] Understanding Generalized Label Smoothing when Learning with Noisy Labels. [Paper]
• [UCSC REAL Lab] A Good Representation Detects Noisy Labels. [Paper]
• [UCSC REAL Lab] Demystifying How Self-Supervised Features Improve Training from Noisy Labels. [Paper][code]
• Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks. [Paper][Code]
• Double Descent in Adversarial Training: An Implicit Label Noise Perspective. [Paper]
• Estimating Instance-dependent Label-noise Transition Matrix using DNNs. [Paper]
• A Theoretical Analysis of Learning with Noisily Labeled Data. [Paper]
• Learning from Multiple Annotators by Incorporating Instance Features. [Paper]
• Learning from Multiple Noisy Partial Labelers. [Paper]
• Instance Correction for Learning with Open-set Noisy Labels. [Paper]
• Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. [Paper]
• Analysis of classifiers robust to noisy labels. [Paper]
• NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? [Paper]
• Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix. [Paper]
• Learning with Noisy Labels by Targeted Relabeling. [Paper]
• Simple Attention Module based Speaker Verification with Iterative noisy label detection. [Paper]
• Adaptive Early-Learning Correction for Segmentation from Noisy Annotations. [Paper]
• Robust Deep Learning from Crowds with Belief Propagation. [Paper]
• Adaptive Hierarchical Similarity Metric Learning with Noisy Labels. [Paper]
• Prototypical Classifier for Robust Class-Imbalanced Learning. [Paper]
• A Survey of Label-noise Representation Learning: Past, Present and Future. [Paper]
• Noisy-Labeled NER with Confidence Estimation. [Paper][Code]
• Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels. [Paper][Code]
• Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels. [Paper][Code]
• Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond. [Paper]
• Understanding the Interaction of Adversarial Training with Noisy Labels. [Paper]
• Learning from Noisy Labels via Dynamic Loss Thresholding. [Paper]
• Evaluating Multi-label Classifiers with Noisy Labels. [Paper]
• Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation. [Paper]
• Transform consistency for learning with noisy labels. [Paper]
• Learning to Combat Noisy Labels via Classification Margins. [Paper]
• Joint Negative and Positive Learning for Noisy Labels. [Paper]
• Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment. [Paper]
• DST: Data Selection and joint Training for Learning with Noisy Labels. [Paper]
• LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment. [Paper]
• A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels. [Paper]
• Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction. [Paper]
• MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels. [Paper]
• On the Robustness of Monte Carlo Dropout Trained with Noisy Labels. [Paper]
• Co-matching: Combating Noisy Labels by Augmentation Anchoring. [Paper]
• Pathological Image Segmentation with Noisy Labels. [Paper]
• CrowdTeacher: Robust Co-teaching with Noisy Answers & Sample-specific Perturbations for Tabular Data. [Paper]
• Approximating Instance-Dependent Noise via Instance-Confidence Embedding. [Paper]
• Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness. [Paper]
• ScanMix: Learning from Severe Label Noise viaSemantic Clustering and Semi-Supervised Learning. [Paper]
• Friends and Foes in Learning from Noisy Labels. [Paper]
• Learning from Noisy Labels for Entity-Centric Information Extraction. [Paper]
• A Fremework Using Contrastive Learning for Classification with Noisy Labels. [Paper]
• Contrastive Learning Improves Model Robustness Under Label Noise. [Paper][Code]
• Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering. [Paper]
• Compensation Learning. [Paper]
• kNet: A Deep kNN Network To Handle Label Noise. [Paper]
• Temporal-aware Language Representation Learning From Crowdsourced Labels. [Paper]
• Memorization in Deep Neural Networks: Does the Loss Function matter?. [Paper]
• Mitigating Memorization in Sample Selection for Learning with Noisy Labels. [Paper]
• P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions. [Paper][Code]
• Decoupling Representation and Classifier for Noisy Label Learning. [Paper]
• Contrastive Representations for Label Noise Require Fine-Tuning. [Paper]
• NGC: A Unified Framework for Learning with Open-World Noisy Data. [Paper]
• Learning From Long-Tailed Data With Noisy Labels. [Paper]
• Robust Long-Tailed Learning Under Label Noise. [Paper]
• Instance-dependent Label-noise Learning under a Structural Causal Model. [Paper]
• Assessing the Quality of the Datasets by Identifying Mislabeled Samples. [Paper]
• Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis. [Paper]
• Robust Temporal Ensembling for Learning with Noisy Labels. [Paper]
• Knowledge Distillation with Noisy Labels for Natural Language Understanding. [Paper]
• Robustness and reliability when training with noisy labels. [Paper]
• Noisy Annotations Robust Consensual Collaborative Affect Expression Recognition. [Paper]
• Consistency Regularization Can Improve Robustness to Label Noise. [Paper]

Papers & Code in 2020

ICML 2020

• [UCSC REAL Lab] Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates. [Paper][Code 1] [Code 2]
• Normalized Loss Functions for Deep Learning with Noisy Labels. [Paper][Code]
• SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. [Paper][Code]
• Error-Bounded Correction of Noisy Labels. [Paper][Code]
• Training Binary Neural Networks through Learning with Noisy Supervision. [Paper][Code]
• Improving generalization by controlling label-noise information in neural network weights. [Paper][Code]
• Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training. [Paper][Code]
• Searching to Exploit Memorization Effect in Learning with Noisy Labels. [Paper][Code]
• Learning with Bounded Instance and Label-dependent Label Noise. [Paper]
• Label-Noise Robust Domain Adaptation. [Paper]
• Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels. [Paper]
• Does label smoothing mitigate label noise?. [Paper]
• Learning with Multiple Complementary Labels. [Paper]
• Deep k-NN for Noisy Labels. [Paper]
• Extreme Multi-label Classification from Aggregated Labels. [Paper]

ICLR 2020

• DivideMix: Learning with Noisy Labels as Semi-supervised Learning. [Paper][Code]
• Learning from Rules Generalizing Labeled Exemplars. [Paper] [Code]
• Robust training with ensemble consensus. [Paper][Code]
• Self-labelling via simultaneous clustering and representation learning. [Paper][Code]
• Can gradient clipping mitigate label noise? [Paper][Code]
• Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification. [Paper][Code]
• Curriculum Loss: Robust Learning and Generalization against Label Corruption. [Paper]
• Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee. [Paper]
• SELF: Learning to Filter Noisy Labels with Self-Ensembling. [Paper]

Nips 2020

• Part-dependent Label Noise: Towards Instance-dependent Label Noise. [Paper][Code]
• Identifying Mislabeled Data using the Area Under the Margin Ranking. [Paper][Code]
• Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. [Paper]
• Early-Learning Regularization Prevents Memorization of Noisy Labels. [Paper][Code]
• Coresets for Robust Training of Deep Neural Networks against Noisy Labels. [Paper][Code]
• Modeling Noisy Annotations for Crowd Counting. [Paper][Code]
• Robust Optimization for Fairness with Noisy Protected Groups. [Paper][Code]
• Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping. [Paper][Code]
• A Topological Filter for Learning with Label Noise. [Paper][Code]
• Self-Adaptive Training: beyond Empirical Risk Minimization. [Paper][Code]
• Disentangling Human Error from the Ground Truth in Segmentation of Medical Images. [Paper][Code]
• Non-Convex SGD Learns Halfspaces with Adversarial Label Noise. [Paper]
• Efficient active learning of sparse halfspaces with arbitrary bounded noise. [Paper]
• Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization. [Paper]
• Labelling unlabelled videos from scratch with multi-modal self-supervision. [Paper][Code]
• Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning. [Paper][Code]
• MetaPoison: Practical General-purpose Clean-label Data Poisoning. [Paper][Code 1][Code 2]
• Provably Consistent Partial-Label Learning. [Paper]
• A Variational Approach for Learning from Positive and Unlabeled Data. [Paper][Code]

AAAI 2020

• [UCSC REAL Lab] Reinforcement Learning with Perturbed Rewards. [Paper] [Code]
• Less Is Better: Unweighted Data Subsampling via Influence Function. [Paper] [Code]
• Weakly Supervised Sequence Tagging from Noisy Rules. [Paper][Code]
• Coupled-View Deep Classifier Learning from Multiple Noisy Annotators. [Paper]
• Partial multi-label learning with noisy label identification. [Paper]
• Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. [Paper]
• Label Error Correction and Generation Through Label Relationships. [Paper]

CVPR 2020

• Combating noisy labels by agreement: A joint training method with co-regularization. [Paper][Code]
• Distilling Effective Supervision From Severe Label Noise. [Paper][Code]
• Self-Training With Noisy Student Improves ImageNet Classification. [Paper][Code]
• Noise Robust Generative Adversarial Networks. [Paper][Code]
• Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition. [Paper]
• DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data. [Paper]
• Spherical Space Domain Adaptation With Robust Pseudo-Label Loss. [Paper][Code]
• Training Noise-Robust Deep Neural Networks via Meta-Learning. [Paper][Code]
• Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data. [Paper][Code]
• Noise-Aware Fully Webly Supervised Object Detection. [Paper][Code]
• Learning From Noisy Anchors for One-Stage Object Detection. [Paper][Code]
• Generating Accurate Pseudo-Labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations. [Paper][Code]
• Revisiting Knowledge Distillation via Label Smoothing Regularization. [Paper][Code]

ECCV 2020

• 2020-ECCV - Learning with Noisy Class Labels for Instance Segmentation. [Paper][Code]
• 2020-ECCV - Suppressing Mislabeled Data via Grouping and Self-Attention. [Paper][Code]
• 2020-ECCV - NoiseRank: Unsupervised Label Noise Reduction with Dependence Models. [Paper]
• 2020-ECCV - Weakly Supervised Learning with Side Information for Noisy Labeled Images. [Paper]
• 2020-ECCV - Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection. [Paper]
• 2020-ECCV - Graph convolutional networks for learning with few clean and many noisy labels. [Paper]

ArXiv 2020

• No Regret Sample Selection with Noisy Labels. [Paper][Code]
• Meta Soft Label Generation for Noisy Labels. [Paper][Code]
• Learning from Noisy Labels with Deep Neural Networks: A Survey. [Paper]
• RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels. [Paper]
• Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning Approach. [Paper]