482 citations · 1.3k across the 38 of their papers we have counts for
39 papers
Exploring Spatial Diversity for Region-based Active Learning
Lile Cai, Xun Xu, Lining Zhang +1
State-of-the-art methods for semantic segmentation are based on deep neural networks trained on large-scale labeled datasets. Acquiring such datasets would incur large annotation c…
Exploring Active Learning for Semiconductor Defect Segmentation
Lile Cai, Ramanpreet Singh Pahwa, Xun Xu +4
The development of X-Ray microscopy (XRM) technology has enabled non-destructive inspection of semiconductor structures for defect identification. Deep learning is widely used as t…
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data
Mohamed Ragab, Emadeldeen Eldele, Min Wu +3
Source-free domain adaptation (SFDA) aims to adapt a pretrained model from a labeled source domain to an unlabeled target domain without access to the source domain data, preservin…
PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation
Dapeng Hu, Jian Liang, Xinchao Wang +1
Unsupervised domain adaptation (UDA) has witnessed remarkable advancements in improving the accuracy of models for unlabeled target domains. However, the calibration of predictive…
Fair yet Asymptotically Equal Collaborative Learning
Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng +2
In collaborative learning with streaming data, nodes (e.g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed…
MoDA: Modeling Deformable 3D Objects from Casual Videos
Chaoyue Song, Jiacheng Wei, Tianyi Chen +4
In this paper, we focus on the challenges of modeling deformable 3D objects from casual videos. With the popularity of neural radiance fields (NeRF), many works extend it to dynami…