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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 126–150 of 249 papers

TitleStatusHype
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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