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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 91–100 of 249 papers

TitleStatusHype
Early Stopping Against Label Noise Without Validation DataCode0
Learning with Noisy Labels: the Exploration of Error Bounds in Classification—0
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
Label Calibration in Source Free Domain Adaptation—0
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labelsCode0
In-Context Learning with Noisy Labels—0
ANNE: Adaptive Nearest Neighbors and Eigenvector-based Sample Selection for Robust Learning with Noisy LabelsCode0
May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels—0
NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification—0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
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