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.
Docta-AI: An advanced data-centric AI platform that detects and rectifies issues in any data format (i.e., label error detection). [Website]
A Hands-on Tutorial for Learning with Noisy Labels (IJCAI 2022)[website]
1st Learning and Mining with Noisy Labels Challenge (IJCAI 2023)[Website][GitHub]
- Benchmarks & Leaderboard
- Papers & Code in 2024
- Papers & Code in 2023
- Papers & Code in 2022
- Papers & Code in 2021
- Papers & Code in 2020
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 (https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise).
- Learning the Latent Causal Structure for Modeling Label Noise. [Paper]
- Entity Alignment with Noisy Annotations from Large Language Models. [Paper]
- Noisy Label Learning with Instance-Dependent Outliers: Identifiability via Crowd Wisdom. [Paper]
- Vision-Language Models are Strong Noisy Label Detectors. [Paper]
- Benchmarking the Reasoning Robustness against Noisy Rationales in Chain-of-thought Prompting. [Paper]
- Learning from Noisy Labels via Conditional Distributionally Robust Optimization. [Paper] *Sample Selection via Contrastive Fragmentation for Noisy Label Regression. [Paper]
- Label Noise: Ignorance Is Bliss. [Paper]
- CoSW: Conditional Sample Weighting for Smoke Segmentation with Label Noise. [Paper]
- Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment. [Paper]
- Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise. [Paper][Code]
- NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise. [Paper]
- Noisy Ostracods: A Fine-Grained, Imbalanced Real-World Dataset for Benchmarking Robust Machine Learning and Label Correction Methods. [Paper]
- Robust Contrastive Multi-view Clustering against Dual Noisy Correspondence. [Paper]
- Perplexity-aware Correction for Robust Alignment with Noisy Preferences. [Paper]
- Information-theoretic Limits of Online Classification with Noisy Labels. [Paper]
- Pi-DUAL: Using privileged information to distinguish clean from noisy labels. [Paper]
- Mitigating Label Noise on Graphs via Topological Sample Selection. [Paper]
- Self-cognitive Denoising in the Presence of Multiple Noisy Label Sources. [Paper]
- Provably Robust DPO: Aligning Language Models with Noisy Feedback. [Paper]
- Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data. [Paper]
- RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences. [Paper]
- FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames. [Paper]
- Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks. [Paper]
- Early Stopping Against Label Noise Without Validation Data. [Paper]
- Label-Noise Robust Diffusion Models. [Paper]
- Why is SAM Robust to Label Noise? [Paper]
- Local Graph Clustering with Noisy Labels. [Paper]
- Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning. [Paper]
- MOFI: Learning Image Representations from Noisy Entity Annotated Images. [Paper]
- Robust Classification via Regression for Learning with Noisy Labels. [Paper]
- TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy Text Fields. [Paper]
- PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels. [Paper][Code]
- Sample-wise Label Confidence Incorporation for Learning with Noisy Labels. [Paper]
- SILT: Shadow-Aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labels. [Paper][Code]
- Graph Matching with Bi-level Noisy Correspondence. [Paper][Code]
- Learning from Noisy Pseudo Labels for Semi-Supervised Temporal Action Localization. [Paper][Code]
- Label-Noise Learning with Intrinsically Long-Tailed Data. [Paper]
- Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups. [Paper]
- LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise. [Paper]
- Holistic Label Correction for Noisy Multi-Label Classification. [Paper][Code]
- When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method. [Paper]
- Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples. [Paper][Code]
- Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels? [Paper][Code]
- RankMatch: Fostering Confidence and Consistency in Learning with Noisy Labels. [Paper]
- Learning from Noisy Data for Semi-Supervised 3D Object Detection. [Paper][Code]
- LNPL-MIL: Learning from Noisy Pseudo Labels for Promoting Multiple Instance Learning in Whole Slide Image. [Paper][Code]
- BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification. [Paper][Code]
- [UCSC REAL Lab] To Aggregate or Not? Learning with Separate Noisy Labels. [Paper]
- DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling. [Paper][Code]
- Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction. [Paper]
- Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler. [Paper][Code]
- Complementary Classifier Induced Partial Label Learning. [Paper][Code]
- Partial-label Learning with Mixed Closed-Set and Open-Set Out-of-Candidate Examples. [Paper]
- Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers. [Paper][Code]
- The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning. [Paper][Code]
- AQuA: A Benchmarking Tool for Label Quality Assessment. [Paper]
- Efficient Testable Learning of Halfspaces with Adversarial Label Noise. [Paper]
- Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data. [Paper][Code]
- Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy. [Paper]
- Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping. [Paper][Code]
- Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model. [Paper]
- Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels. [Paper][Code]
- SoTTA: Robust Test-Time Adaptation on Noisy Data Streams. [Paper][Code]
- Active Negative Loss Functions for Learning with Noisy Labels. [Paper][Code]
- Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels. [Paper][Code]
- Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign? [Paper]
- CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels. [Paper][Code]
- Deep Insights into Noisy Pseudo Labeling on Graph Data. [Paper]
- ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections. [Paper][Code]
- ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning. [Paper][Code]
- Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping. [Paper][Code]
- Label Poisoning is All You Need. [Paper][Code]
- SLaM: Student-Label Mixing for Distillation with Unlabeled Examples. [Paper]
- IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers. [Paper]
- HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text. [Paper][Code]
- [UCSC REAL Lab] Identifiability of Label Noise Transition Matrix. [Paper]
- Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? [Paper]
- Mitigating Memorization of Noisy Labels by Clipping the Model Prediction. [Paper][Code]
- CrossSplit: Mitigating Label Noise Memorization through Data Splitting. [Paper][Code]
- Understanding Self-Distillation in the Presence of Label Noise. [Paper]
- RandomClassificationNoisedoesnotdefeatAllConvexPotentialBoosters IrrespectiveofModelChoice. [Paper]
- Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach. [Paper]
- Delving into Noisy Label Detection with Clean Data. [Paper]
- When does Privileged information Explain Away Label Noise? [Paper]
- Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective. [Paper][Code]
- Promises and Pitfalls of Threshold-based Auto-labeling. [Paper]
- Accelerating Exploration with Unlabeled Prior Data. [Paper]
- Twin Contrastive Learning with Noisy Labels. [Paper][Code]
- Exploring High-Quality Pseudo Masks for Weakly Supervised Instance Segmentation. [Paper][Code]
- HandsOff: Labeled Dataset Generation with No Additional Human Annotations. [Paper][Code]
- Learning from Noisy Labels with Decoupled Meta Label Purifier. [Paper][Code]
- DISC: Learning from Noisy Labels via Dynamic Instance-Specific Selection and Correction. [Paper][Code]
- Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels. [Paper]
- Fine-Grained Classification with Noisy Labels. [Paper]
- Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies. [Paper][Code]
- MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-supervised Object Detection. [Paper][Code]
- OT-Filter: An Optimal Transport Filter for Learning With Noisy Labels. [Paper]
- Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection. [Paper][Code]
- Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module. [Paper][Code]
- Learning with Noisy labels via Self-supervised Adversarial Noisy Masking. [Paper][Code]
- RONO: Robust Discriminative Learning with Noisy Labels for 2D-3D Cross-Modal Retrieval. [Paper][Code]
- [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]
- 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]
- 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]
- [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.
- 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]
- [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]
- 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]
- [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]
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]
- 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]
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]
- [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]
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]
Conference date: Apr 13, 2021 -- Apr 15, 2021
- Collaborative Classification from Noisy Labels. [Paper]
- Linear Models are Robust Optimal Under Strategic Behavior. [Paper]
- 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]
- (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]
- [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]
- [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]
- 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]
- 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]
- [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]
- 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]
- 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]
- 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]
