4 papers
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
Ruofan Hu, Dongyu Zhang, Huayi Zhang +1
Learning with noisy labels (LNL) is essential for training deep neural networks with imperfect data. Meta-learning approaches have achieved success by using a clean unbiased labele…
CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance
Dongyu Zhang, Ruofan Hu, Elke Rundensteiner
Deep neural networks (DNNs) have advanced many machine learning tasks, but their performance is often harmed by noisy labels in real-world data. Addressing this, we introduce CoLaf…
UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection
Ruofan Hu, Dongyu Zhang, Dandan Tao +3
Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodb…
TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks
Ruofan Hu, Dongyu Zhang, Dandan Tao +3
Foodborne illness is a serious but preventable public health problem -- with delays in detecting the associated outbreaks resulting in productivity loss, expensive recalls, public…