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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
Partial Label Supervision for Agnostic Generative Noisy Label LearningCode0
Foster Adaptivity and Balance in Learning with Noisy LabelsCode0
Learning with Noisy Labels through Learnable Weighting and Centroid SimilarityCode0
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type PerspectiveCode0
Learning to Learn from Noisy Labeled DataCode0
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation LearningCode0
Learning to Rectify for Robust Learning with Noisy LabelsCode0
Learning to Detect and Retrieve Objects from Unlabeled VideosCode0
Confident Learning: Estimating Uncertainty in Dataset LabelsCode0
Probabilistic End-to-end Noise Correction for Learning with Noisy LabelsCode0
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language ProcessingCode0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
Latent Class-Conditional Noise ModelCode0
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
Late Stopping: Avoiding Confidently Learning from Mislabeled ExamplesCode0
No Regret Sample Selection with Noisy LabelsCode0
ANNE: Adaptive Nearest Neighbors and Eigenvector-based Sample Selection for Robust Learning with Noisy LabelsCode0
An Instance-Dependent Simulation Framework for Learning with Label NoiseCode0
Safeguarded Dynamic Label Regression for Generalized Noisy SupervisionCode0
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
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labelsCode0
L_DMI: An Information-theoretic Noise-robust Loss FunctionCode0
SIGUA: Forgetting May Make Learning with Noisy Labels More RobustCode0
Learning with Neighbor Consistency for Noisy LabelsCode0
SELFIE: Refurbishing Unclean Samples for Robust Deep LearningCode0
Robust Loss Functions for Training Decision Trees with Noisy LabelsCode0
A Unified Framework for Connecting Noise Modeling to Boost Noise DetectionCode0
Dynamic Loss For Robust LearningCode0
LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge IntegrationCode0
Early Stopping Against Label Noise Without Validation DataCode0
Enhanced Meta Label Correction for Coping with Label CorruptionCode0
Benchmarking Label Noise in Instance Segmentation: Spatial Noise MattersCode0
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
Blind Knowledge Distillation for Robust Image ClassificationCode0
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy LabelsCode0
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels—0
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels—0
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning—0
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks—0
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