This article takes a problem-oriented perspective and presents a comprehensive review of
transfer-learning methods, both shallow and deep, for cross-dataset visual recognition …

Domain adaptation aims to leverage knowledge from a well-labeled source domain to a
poorly labeled target domain. A majority of existing works transfer the knowledge at either …

Currently, unsupervised heterogeneous domain adaptation in a generalized setting, which
is the most common scenario in real-world applications, is under insufficient exploration …

In real-world transfer learning tasks, especially in cross-modal applications, the source
domain and the target domain often have different features and distributions, which are well …

Learning methods are challenged when there is not enough labelled data. It gets worse
when the existing learning data have different distributions in different domains. To deal with …

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by a
wide range of symptoms. Identifying biomarkers for accurate diagnosis is crucial for early …

Transfer learning has been proven to be effective for the problems where training data from
a source domain and test data from a target domain are drawn from different distributions. To …

Unsupervised domain adaptation has caught appealing attentions as it facilitates the
unlabeled target learning by borrowing existing well-established source domain knowledge …

Subspace learning and reconstruction have been widely explored in recent transfer learning
work. Generally, a specially designed projection and reconstruction transfer functions …

Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard
technique for achieving state-of-the-art performance on computer vision benchmarks …