Abstract Domain adaptation aims to transfer auxiliary knowledge from a source domain to
enhance the learning performance on a target domain. Recent studies have suggested that …

Transfer learning, which leverages labeled data in a source domain to train an accurate
classifier for classification tasks in a target domain, has attracted extensive research …

In the problem of domain transfer learning, we learn a model for the prediction in a target
domain from the data of both some source domains and the target domain, where the target …

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 …

Transfer learning usually utilizes the knowledge learned from the relative labeled source
domain to promote the model performance in the unlabeled or few labeled target domain …

Transferring knowledge from one domain to another is challenging due to a number of
reasons. Since both conditional and marginal distribution of the training data and test data …

In machine learning problems, labeled data are often in short supply. One of the feasible
solution for this problem is transfer learning. It can make use of the labeled data from other …

Transfer learning has achieved promising results by leveraging knowledge from the source
domain to annotate the target domain which has few or none labels. Existing methods often …

Open datasets serve as facilitators for researchers to conduct research with ground truth
data. Generally, datasets contain innovation and knowledge in the domains that could be …

Transfer learning has been demonstrated to be effective for many real-world applications as
it exploits knowledge present in labeled training data from a source domain to enhance a …