Learning-with-Noisy-Labels

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

Competition

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

June 15, 2022: Deadline for participation registration

Content

Benchmarks & Leaderboard
Papers & Code in 2022
Papers & Code in 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:

Dataset	Leaderboard Link	Website	Paper
CIFAR-10N	[Leaderboard]	[Website]	[Paper]
CIFAR-100N	[Leaderboard]	[Website]	[Paper]
Red Stanford Cars	N/A	[Website]	[Paper]
Red Mini-ImageNet	N/A	[Website]	[Paper]
Animal-10N	[Leaderboard]	[Website]	[Paper]
Food-101N	N/A	[Website]	[Paper]
Clothing1M	[Leaderboard]	[Website]	[Paper]

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

Papers & Code in 2022

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

ICML 2022

[UCSC REAL Lab] To Smooth or Not? When Label Smoothing Meets Noisy Labels. [Paper]
[UCSC REAL Lab] Detecting Corrupted Labels Without Training a Model to Predict. [Paper]
[UCSC REAL Lab] Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features. [Paper]
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.
Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent.
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]

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advances-in-label-noise-learning's Introduction