Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

For a target task where the labeled data are unavailable, domain adaptation can transfer a
learner from a different source domain. Previous deep domain adaptation methods mainly …

Conventional unsupervised domain adaptation (UDA) assumes that training data are
sampled from a single domain. This neglects the more practical scenario where training data …

Deep domain adaptation has emerged as a new learning technique to address the lack of
massive amounts of labeled data. Compared to conventional methods, which learn shared …

This article proposes an unsupervised deep learning–based approach to detect structural
damage. Supervised deep learning methods have been proposed in recent years, but they …

Unsupervised domain adaptation addresses the problem of transferring knowledge from a
well-labeled source domain to an unlabeled target domain where the two domains have …

The recent advances in deep transfer learning reveal that adversarial learning can be
embedded into deep networks to learn more transferable features to reduce the distribution …

Abstract Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a different but …

In this paper, we propose an elegant solution that is directly addressing the bottlenecks of
the traditional deep learning approaches and offers an explainable internal architecture that …