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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 141–150 of 249 papers

TitleStatusHype
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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