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A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
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 …
attention because of its promise to further optimize process design, quality control, health …
Domain adaptive faster r-cnn for object detection in the wild
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 …
distribution, which, however, does not always hold in practice. Such a distribution mismatch …
A survey of unsupervised deep domain adaptation
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 …
approaches for supervised learning have performed well, they assume that training and …
A decade survey of transfer learning (2010–2020)
Transfer learning (TL) has been successfully applied to many real-world problems that
traditional machine learning (ML) cannot handle, such as image processing, speech …
traditional machine learning (ML) cannot handle, such as image processing, speech …
Semi-supervised domain adaptation via minimax entropy
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 …
of source and target domains without any target supervision. However, we show that these …