A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies

A Thelen, X Zhang, O Fink, Y Lu, S Ghosh… - Structural and …, 2022 - Springer
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

Domain adaptive faster r-cnn for object detection in the wild

Y Chen, W Li, C Sakaridis, D Dai… - Proceedings of the …, 2018 - openaccess.thecvf.com
Object detection typically assumes that training and test data are drawn from an identical
distribution, which, however, does not always hold in practice. Such a distribution mismatch …

A survey of unsupervised deep domain adaptation

G Wilson, DJ Cook - ACM Transactions on Intelligent Systems and …, 2020 - dl.acm.org
Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

A decade survey of transfer learning (2010–2020)

S Niu, Y Liu, J Wang, H Song - IEEE Transactions on Artificial …, 2020 - ieeexplore.ieee.org
Transfer learning (TL) has been successfully applied to many real-world problems that
traditional machine learning (ML) cannot handle, such as image processing, speech …

Semi-supervised domain adaptation via minimax entropy

K Saito, D Kim, S Sclaroff, T Darrell… - Proceedings of the …, 2019 - openaccess.thecvf.com
Contemporary domain adaptation methods are very effective at aligning feature distributions
of source and target domains without any target supervision. However, we show that these …