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Learning with noisy labels

Learning with noisy labels means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data.

Papers

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Showing 101–150 of 249 papers

TitleStatusHype
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation LearningCode0
Partial Label Supervision for Agnostic Generative Noisy Label LearningCode0
No Regret Sample Selection with Noisy LabelsCode0
Confident Learning: Estimating Uncertainty in Dataset LabelsCode0
Probabilistic End-to-end Noise Correction for Learning with Noisy LabelsCode0
ANNE: Adaptive Nearest Neighbors and Eigenvector-based Sample Selection for Robust Learning with Noisy LabelsCode0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
Learning advisor networks for noisy image classificationCode0
Cross-to-merge training with class balance strategy for learning with noisy labelsCode0
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy LabelsCode0
Symmetric Cross Entropy for Robust Learning with Noisy LabelsCode0
Debiased Sample Selection for Combating Noisy LabelsCode0
Learning with Noisy Labels by Adaptive Gradient-Based Outlier RemovalCode0
LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge IntegrationCode0
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
Making Deep Neural Networks Robust to Label Noise: a Loss Correction ApproachCode0
SELFIE: Refurbishing Unclean Samples for Robust Deep LearningCode0
Detect and Correct: A Selective Noise Correction Method for Learning with Noisy LabelsCode0
Dimensionality-Driven Learning with Noisy LabelsCode0
Are Anchor Points Really Indispensable in Label-Noise Learning?Code0
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type PerspectiveCode0
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labelsCode0
A Unified Framework for Connecting Noise Modeling to Boost Noise DetectionCode0
SIGUA: Forgetting May Make Learning with Noisy Labels More RobustCode0
How does Disagreement Help Generalization against Label Corruption?Code0
Benchmarking Label Noise in Instance Segmentation: Spatial Noise MattersCode0
Blind Knowledge Distillation for Robust Image ClassificationCode0
Meta Label Correction for Noisy Label LearningCode0
Dynamic Loss For Robust LearningCode0
Bootstrapping the Relationship Between Images and Their Clean and Noisy LabelsCode0
Early Stopping Against Label Noise Without Validation DataCode0
Enhanced Meta Label Correction for Coping with Label CorruptionCode0
Learning to Detect and Retrieve Objects from Unlabeled VideosCode0
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy LabelsCode0
Exploring the Robustness of In-Context Learning with Noisy LabelsCode0
Exploring Video-Based Driver Activity Recognition under Noisy LabelsCode0
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered BeneficialCode0
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy LabelsCode0
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Can We Treat Noisy Labels as Accurate?Code0
Noise against noise: stochastic label noise helps combat inherent label noise—0
Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization—0
NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification—0
Understanding Sharpness-Aware Minimization—0
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?—0
A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
An Instance-Dependent Simulation Framework for Learning with Label Noise—0
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels—0
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