2 citations · 9 across the 6 of their papers we have counts for
7 papers
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood Context
Jing Wang, Wonho Bae, Jiahong Chen +3
Source-free domain adaptation (SFDA) involves adapting a model originally trained using a labeled dataset ({\em source domain}) to perform effectively on an unlabeled dataset ({\em…
Contrastive Adversarial Training for Unsupervised Domain Adaptation
Jiahong Chen, Zhilin Zhang, Lucy Li +2
Domain adversarial training has shown its effective capability for finding domain invariant feature representations and been successfully adopted for various domain adaptation task…
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation
Kuangen Zhang, Jiahong Chen, Jing Wang +4
Recognizing human locomotion intent and activities is important for controlling the wearable robots while walking in complex environments. However, human-robot interface signals ar…
Preserving Domain Private Representation via Mutual Information Maximization
Jiahong Chen, Jing Wang, Weipeng Lin +2
Recent advances in unsupervised domain adaptation have shown that mitigating the domain divergence by extracting the domain-invariant representation could significantly improve the…
Data-driven Sensor Deployment for Spatiotemporal Field Reconstruction
Jiahong Chen
This paper concerns the data-driven sensor deployment problem in large spatiotemporal fields. Traditionally, sensor deployment strategies have been heavily dependent on model-based…
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment
Jing Wang, Jiahong Chen, Jianzhe Lin +2
In this study, we focus on the unsupervised domain adaptation problem where an approximate inference model is to be learned from a labeled data domain and expected to generalize we…
Optimization of Wireless Sensor Network Deployment for Spatiotemporal Reconstruction and Prediction
Jiahong Chen, Teng Li, Jing Wang +1
This paper addresses the problem of optimizing sensor deployment locations to reconstruct and also predict a spatiotemporal field. A novel deep learning framework is developed to f…