2 papers
cs.LG2025
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…
cs.CL2023
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…