19 citations · 43 across the 9 of their papers we have counts for
5 papers · 1 filter
Federated Active Learning Under Extreme Non-IID and Global Class Imbalance
Chen-Chen Zong, Sheng-Jun Huang
Federated active learning (FAL) seeks to reduce annotation cost under privacy constraints, yet its effectiveness degrades in realistic settings with severe global class imbalance a…
Bidirectional Uncertainty-Based Active Learning for Open Set Annotation
Chen-Chen Zong, Ye-Wen Wang, Kun-Peng Ning +2
Active learning (AL) in open set scenarios presents a novel challenge of identifying the most valuable examples in an unlabeled data pool that comprises data from both known and un…
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels
Chen-Chen Zong, Ye-Wen Wang, Ming-Kun Xie +1
Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs). Existing approaches address this issue through loss correction or…
Multi-Label Knowledge Distillation
Penghui Yang, Ming-Kun Xie, Chen-Chen Zong +4
Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, whi…
Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting
Chen-Chen Zong, Zheng-Tao Cao, Hong-Tao Guo +4
Deep neural networks trained with standard cross-entropy loss are more prone to memorize noisy labels, which degrades their performance. Negative learning using complementary label…