GitHub - weijiaheng/Advances-in-Label-Noise-Learning: A curated (most recent) list of resources for Learning with Noisy Labels

GitHub - FF-Wong/Deep-learning-methods-for-Hyperspectral-Image-Denoising: Deep learning methods for Hyperspectral Image Denoising

GitHub - junjun-jiang/Hyperspectral-Image-Denoising-Benchmark: A list of hyperspectral image denoising resources collected by Yongsen Zhao and Junjun Jiang.

Learning-with-Noisy-Labels

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]

This repo focus on papers after 2019, for previous works, please refer to (GitHub - subeeshvasu/Awesome-Learning-with-Label-Noise: A curated list of resources for Learning with Noisy Labels).

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]
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]

Hyperspectral Image Denoising

Deep Learning Methods

Hyperspectral Imagery Denoising by Deep Learning With Trainable Nonlinearity Function, GRSL 2017, Weiying Xie et al.[Page]
[HSI-DeNet]: Hyperspectral Image Restoration via Convolutional Neural Network, IEEE TGRS 2018, Yi Chang et al.[Page][Web][Pdf]
[HSID-CNN]:Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network, IEEE TGRS 2018, Qiangqiang Yuan et al.[Page][Code]
[Deep Hyperspectral Prior]: Denoising, Inpainting, Super-Resolution, ICCV Workshops 2019, Oleksii Sidorov et al.[Page][Code] [Pdf]
A 3-D Atrous Convolution Neural Network for Hyperspectral Image Denoising, IEEE TGRS 2019, Wei Liu et al.[Page]
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network, IEEE TGRS 2019, Qiang Zhang et al.[Page][Code][Pdf]
Deep Spatial-spectral Representation Learning for Hyperspectral Image Denoising, IEEE TCI 2019, Weisheng Dong et al. [Page] [Code]
Zero-Shot Hyperspectral Image Denoising With Separable Image Prior, ICCV Workshops 2019, Ryuji Imamura et al.[Page]
Source:Self-supervised Hyperspectral Image Restoration using Separable Image Prior, arxiv 2019, Ryuji Imamura et al.[Page]
DNN-based Hyperspectral Image Denoising with Spatio-spectral Pre-training, IEEE GCCE 2019, Tatsuki Itasaka et al.[Page]
Efficient Convolutional Neural Network for Spectral-Spatial Hyperspectral Denoising, WHISPERS 2019, Alessandro Maffei et al.[Page]

2020

[HSI-SDeCNN]:A Single Model CNN for Hyperspectral, IEEE TGRS 2020 , Alessandro Maffei et al.[Page][Pdf][Code]
3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising, IEEE TNNLS 2020, Kaixuan Wei et al.[Page][Code][Pdf]
Hyperspectral Image Denoising Using SURE-Based Unsupervised Convolutional Neural Networks, IEEE TGRS 2020 , Han V. Nguyen et al.[Page]
ADRN: Attention-based deep residual network for hyperspectral image denoising, ICASSP 2020 , Yongsen Zhao et al.[Page][Pdf]
A New Multiscale Residual Learning Network for HSI Inconsistent Noise Removal, GRSL 2020 , Yuan Yuan et al.[Page]
A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods, arxiv 2020 , Zhaoming Kong et al.[Page]

2021

Hyperspectral Image Denoising Using a 3-D Attention Denoising Network, TGRS 2021 , Qian Shi et al.[Page]
Hyperspectral Image Denoising Based On Multi-Stream Denoising Network, IGARSS 2021 , Yan Gao et al.[Page]
A Self-Supervised Denoising Network for Satellite-Airborne-Ground Hyperspectral Imagery, TGRS 2021 , Xinyu Wang et al.[Page]
Partial-DNet: A Novel Blind Denoising Model With Noise Intensity Estimation for HSI, TGRS 2021 , Yuan Yuan et al.[Page]
Deep Spatial-Spectral Global Reasoning Network for Hyperspectral Image Denoising, TGRS 2021 , Xiangyong Cao et al.[Page][Code]
Attention-Based Octave Network for Hyperspectral Image Denoising, JSTARS 2021 , Ziwen Kan et al.[Page][Code]
SSCAN: A Spatial-spectral Cross Attention Network for Hyperspectral Image Denoising, arxiv 2021 , Zhiqiang Wang et al.[Page]
Hyperspectral Image Restoration with Self-supervised Learning: A Two-stage Training Approach, TGRS 2021 , Yuntao Qian et al.[Page]

2022

DnRCNN: Deep Recurrent Convolutional Neural Network for HSI Destriping, TNNLS 2022 , Juntao Guan et al.[Page]
Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks, RS 2022 , Chengjun Wang et al.[Page]
Deep plug-and-play prior for hyperspectral image restoration, Neurocomputing 2022 , Zeqiang Lai et al.[Page]

Hybrid models

Hyperspectral Image Denoising via Matrix Factorization and Deep Prior Regularization, IEEE TIP 2019, Baihong Lin et al[Page]
Hyperspectral image restoration via CNN denoiser prior regularized low-rank tensor recovery, CVIU 2020, H. Zeng et al..[Page]
Deep spatio-spectral Bayesian posterior for hyperspectral image non-i.i.d. noise removal, ISPRS JPRS 2020, Qiang Zhang et al.[Page][Pdf]
SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising, arXiv 2020 , Fengchao Xiong et al.[Page]
Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior, arxiv 2020, Yi-Si Luo et al.[Page]
Hyperspectral Mixed Noise Removal via Spatial-Spectral Constrained Unsupervised Deep Image Prior, JSTARS 2021 , Yi-Si Luo et al.[Page]
Hyperspectral Image Denoising via Clustering-Based Latent Variable in Variational Bayesian Framework, TGRS 2021, Peyman Azimpour et al.[Page]
Hyperspectral Denoising Via Global Tensor Ring Decomposition and Local Unsupervised Deep Image Prior, IGARSS 2021 , Jian-Li Wang et al.[Page]
Learning a Model-Based Deep Hyperspectral Denoiser from a Single Noisy Hyperspectral Image, IGARSS 2021 , Guanyiman Fu et al.[Page]
Hyperspectral Denoising Using Unsupervised Disentangled Spatiospectral Deep Priors, TGRS 2021 , Yu-Chun Miao et al.[Page]
LR-Net: Low-Rank Spatial-Spectral Network for Hyperspectral Image Denoising, TIP 2021 , Hongyan Zhang et al.[Page]
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration, NeurIPS 2021 , Theo Bodrito et al.[Page][Code]
MAC-Net: Model Aided Nonlocal Neural Network for Hyperspectral Image Denoising, TGRS 2021 , Fengchao Xiong et al.[Page]
Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior, RS 2021 , Lina Zhuang et al.[Page]
Hyperspectral Image Denoising via Low-Rank Representation and CNN Denoiser, JSTARS 2021 , Hezhi Sun et al.[Page]
FastHyMix: Fast and Parameter-Free Hyperspectral Image Mixed Noise Removal, TNNLS 2021 , Lina Zhuang et al.[Page][Code]

Temporarily uncategorized

Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising, IJCNN 2020 , Yuqiao Liu et al.[Page]
A novel fusion paradigm for multi-channel image denoising, Information Fusion 2020 , Yue Wu et al.[Page]

Other tasks

HSI-IPNet: Hyperspectral Imagery Inpainting by Deep Learning With Adaptive Spectral Extraction, IEEE J-STARS 2020, Rong Wong et al.[Page][Pdf]
Fast stripe noise removal from hyperspectral image via multi-scale dilated unidirectional convolution, Multimedia Tools and Applications 2020, Ziying Wang et al.[Page][Code]
Toward Universal Stripe Removal via Wavelet-Based Deep Convolutional Neural Network, IEEE TGRS 2020, Yi Chang et al.[Page]
Learning Spectral-Spatial Prior Via 3DDNCNN for Hyperspectral Image Deconvolution, ICASSP 2020, Xiuheng Wang et al.[Page]
DnRCNN: Deep Recurrent Convolutional Neural Network for HSI Destriping, TNNLS 2022 , Juntao Guan et al.[Page]

Reconstruction

Hyperspectral Image Reconstruction Using a Deep Spatial-Spectral Prior, CVPR 2019 , Lizhi Wang et al.[Page][Pdf]
HyperMixNet: Hyperspectral Image Reconstruction With Deep Mixed Network From a Snapshot Measurement, ICCV Workshops 2021 , Kouhei Yorimoto et al.[Page]

Extra

Deep learning on image denoising: An overview, Neural Networks 2020, Chunwei Tian et al.[Page]
A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods, arxiv 2020, Zhaoming Kong et al.[Page]
Hyperspectral Image Denoising with Realistic Data, ICCV 2021 , Tao Zhang et al.[Page][Code]
Image Restoration for Remote Sensing: Overview and Toolbox, arxiv 2021, Benhood Rasti et al.[Page][Code]
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package, arxiv 2022, Daniel Coquelin et al.[Page][Code]

Datasets

Image Quality Measurement

Peak Signal to Noise Ratio (PSNR)
Structural SIMilarity index (SSIM)
Spectral Angle Mapper (SAM)
Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS)
Feature SIMilarity index (FSIM)

【论文合集】Awesome Label Noise Learning and Image Denoising