Conventional deep CNN-based segmentation approaches have achieved satisfactory
performance in recent years, however, they are essentially Big Data-driven technologies …

Domain adaptation (DA) aims at facilitating the target model training by leveraging
knowledge from related but distribution-inconsistent source domain. Most of the previous DA …

Major Depressive Disorder (MDD) detection with cross-domain datasets is a crucial yet
challenging application due to the data scarcity and isolated data island issues in …

Multi-source domain adaptation is a challenging topic in transfer learning, especially when
the data of each domain are represented by different kinds of features, ie, Multi-source …

The application of transfer learning, leveraging knowledge from source domains to enhance
model performance in a target domain, has significantly grown, supporting diverse real …

One of the main challenges in multi-source domain adaptation is how to reduce the domain
discrepancy between each source domain and a target domain, and then evaluate the …

Heterogeneous domain adaptation seeks to learn an effective classifier or regression model
for unlabeled target samples by using the well-labeled source samples but residing in …

Heterogeneous domain adaptation (HDA) is a challenging problem because of the different
feature representations in the source and target domains. Most HDA methods search for …

Domain adaptation aims at improving the performance of learning tasks in a target domain
by leveraging the knowledge extracted from a source domain. To this end, one can perform …

It remains a challenging problem for data classification without training patterns. In many
applications, there may exist some labeled data in other related domains (called source …