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 …

Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for
the target domain, in which only unlabeled and a small number of labeled data are …

In unsupervised domain adaptation (UDA), a classifier for a target domain is trained with
labeled source data and unlabeled target data. Existing UDA methods assume that the …

Cross-company defect prediction (CCDP) learns a prediction model by using training data
from one or multiple projects of a source company and then applies the model to the target …

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI)
has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) …

Domain adaptation leverages the knowledge in one domain-the source domain-to improve
learning efficiency in another domain-the target domain. Existing heterogeneous domain …

In this paper, we propose an approach to the domain adaptation, dubbed Second-or Higher-
order Transfer of Knowledge (So-HoT), based on the mixture of alignments of second-or …

Cross-project defect prediction (CPDP) refers to predicting defects in a target project using
prediction models trained from historical data of other source projects. And CPDP in the …

Single-modal neuroimaging-based diagnosis for brain diseases is a main routine due to the
lack of advanced imaging devices, especially in rural hospitals. Transfer learning (TL) has …