Supervised deep learning has achieved remarkable success in various applications.
Successful machine learning application however depends on the availability of sufficiently …

This paper presents the domain generalization methods Multi-Domain Transfer Component
Analysis (Multi-TCA) and Multi-Domain Semi-Supervised Transfer Component Analysis …

During the past decade, deep neural networks have led to fast-paced progress and
significant achievements in computer vision problems, for both academia and industry. Yet …

Deep learning works well when the problem is regular enough and there is abundant
training data to adequately and in a representative way reflect all the regularity. As the …

With the development of deep transfer learning, the generalization abilities of models in
similar scenarios have been significantly improved. However, for regression tasks, either the …

Abstract Domain generalization is a sub-field of transfer learning that aims at bridging the
gap between two different domains in the absence of any knowledge about the target …

Abstract Activities Recognition (AR) and Occupancy Estimation (OE) are topics of current
interest. AR and OE help many smart building applications such as energy systems and …

Existing domain generalization aims to learn a generalizable model to perform well even on
unseen domains. For many real-world machine learning applications, the data distribution …

Accurately extracting buildings from aerial images has essential research significance for
timely understanding human intervention on the land. The distribution discrepancies …

Deep neural networks often suffer performance degradation when the testing data
distribution differs significantly from the training data distribution. To address this problem …