… Domain adaptation and transfer learning are sub-fields within machine learning that are …
risk minimization, and how transfer learning and domain adaptation expand upon this framework…

… eral popular benchmark datasets in domain adaptation and transfer learning show that our
… first learning to learn approach usable on domain adaptation and transfer learning problems …

… In particular, deep domain adaptation gets the most … transfer learning in this research, we
pay our attention to analyzing and discussing the recent advances in deep domain adaptation. …

… of the techniques using deep learning in domain adaptation and their applications in text …
, and few-shot learning that have gained popularity in the domain adaptation field. We perform …

… during the transfer learning process for domain adaptation. … on the target domain is required
for domain adaptation with … We use transfer learning methodology for domain adaptation …

… This paper proposes a novel transfer learning method Multi-Layer Transfer Learning (MLTL).
MLTL first generates specific latent feature spaces. Second, it combines these specific …

… In [26], Chan and NG proposed to adapt existing Word Sense Disambiguation (WSD)
systems to a target domain by using domain adaptation techniques and employing an active …

… Domain adaptation aims at training a classifier in one … One successfully used framework of
domain adaptation is to learn a … a new domain adaptation framework named Deep Transfer …

… idea of transfer learning, a domain adaptation extreme learning … transferring the useful
information from different operating conditions to the existing soft sensor, the prediction domain is …

… device (the source domain) for a new mobile device (the target domain). Domain adaptation
can be considered as a special setting of transfer learning which aims at transferring shared …