19 citations · 60 across the 5 of their papers we have counts for
5 papers
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…
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation
Feng Sun, Ming-Kun Xie, Sheng-Jun Huang
In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels an…
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise
Ming-Kun Xie, Sheng-Jun Huang
Class-conditional noise commonly exists in machine learning tasks, where the class label is corrupted with a probability depending on its ground-truth. Many research efforts have b…
Active Feature Acquisition with Supervised Matrix Completion
Sheng-Jun Huang, Miao Xu, Ming-Kun Xie +3
Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance. While feature…