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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

Showing 110 of 249 papers

TitleStatusHype
NLPrompt: Noise-Label Prompt Learning for Vision-Language ModelsCode2
Sharpness-Aware Minimization for Efficiently Improving GeneralizationCode2
SURE: SUrvey REcipes for building reliable and robust deep networksCode2
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Active Negative Loss: A Robust Framework for Learning with Noisy LabelsCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
CLIPCleaner: Cleaning Noisy Labels with CLIPCode1
Asymmetric Loss Functions for Learning with Noisy LabelsCode1
Boosting Co-teaching with Compression Regularization for Label NoiseCode1
AlleNoise: large-scale text classification benchmark dataset with real-world label noiseCode1
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