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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 176–200 of 249 papers

TitleStatusHype
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy LabelsCode0
Prototypical Classifier for Robust Class-Imbalanced Learning—0
Clean or Annotate: How to Spend a Limited Data Collection Budget—0
Robust Temporal Ensembling for Learning with Noisy Labels—0
Understanding Sharpness-Aware Minimization—0
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels—0
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples—0
Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction?—0
Relative Instance Credibility Inference for Learning with Noisy Labels—0
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis—0
Confidence Adaptive Regularization for Deep Learning with Noisy Labels—0
Cooperative Learning for Noisy Supervision—0
An Instance-Dependent Simulation Framework for Learning with Label Noise—0
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered BeneficialCode0
Mitigating Memorization in Sample Selection for Learning with Noisy Labels—0
Distilling effective supervision for robust medical image segmentation with noisy labels—0
DualGraph: A Graph-Based Method for Reasoning About Label Noise—0
Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels—0
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels—0
Joint Text and Label Generation for Spoken Language Understanding—0
Transform consistency for learning with noisy labels—0
Co-matching: Combating Noisy Labels by Augmentation Anchoring—0
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels—0
Learning with Group Noise—0
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
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