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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 71–80 of 249 papers

TitleStatusHype
Bayesian Optimization Meets Self-DistillationCode1
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data—0
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in MoviesCode1
Learning with Noisy Labels through Learnable Weighting and Centroid SimilarityCode0
Twin Contrastive Learning with Noisy LabelsCode1
Fine-Grained Classification with Noisy Labels—0
Latent Class-Conditional Noise ModelCode0
Learning with Noisy labels via Self-supervised Adversarial Noisy MaskingCode1
When Source-Free Domain Adaptation Meets Learning with Noisy Labels—0
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy LabelsCode1
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