Abstract Knowledge transfer from multiple source domains to a target domain is crucial in
transfer learning. Most existing methods are focused on learning weights for different …

Recent years have witnessed an increased interest in transfer learning. Despite the vast
amount of research performed in this field, there are remaining challenges in applying the …

We propose a framework for learning new target tasks by leveraging existing heterogeneous
knowledge sources. Unlike the traditional transfer learning, we do not require explicit …

Transfer learning is a great technology that can leverage knowledge from label-rich domains
to address problems in similar domains that lack labeled data. Most previous works focus on …

Transfer learning has attracted a lot of attention in the past decade. One crucial research
issue in transfer learning is how to find a good representation for instances of different …

Transfer learning has gained a lot of attention and interest in the past decade. One crucial
research issue in transfer learning is how to find a good representation for instances of …

Transfer learning aims to leverage the knowledge in the source domain to facilitate the
learning tasks in the target domain. It has attracted extensive research interests recently due …

Transfer learning addresses the problem of how to leverage acquired knowledge from a
source domain to improve the learning efficiency and accuracy of the target domain that has …

The effectiveness of knowledge transfer using classification algorithms depends on the
difference between the distribution that generates the training examples and the one from …

Transfer learning extracts useful information from the related source domain and leverages it
to promote the target learning. The effectiveness of the transfer was affected by the …