The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Conventional domain adaptation (DA) techniques aim to improve domain transferability by
learning domain-invariant representations; while concurrently preserving the task …

Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from multiple
labeled source domains to an unlabeled target domain. Although current methods achieve …

The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift
between the source and target domains. Prior DA works show that pretext tasks could be …

Abstract Conventional Unsupervised Domain Adaptation (UDA) strives to minimize
distribution discrepancy between domains which neglects to harness rich semantics from …

Abstract Domain adaptation (DA) aims to transfer knowledge from a label-rich source
domain to a related but label-scarce target domain. Recently, increasing research has …

In general, videos are powerful at recording physical patterns (eg, spatial layout) while texts
are great at describing abstract symbols (eg, emotion). When video and text are used in …

Most existing methods for unsupervised domain adaptation (UDA) rely on a shared network
to extract domain-invariant features. However, when facing multiple source domains …

Unsupervised multidomain adaptation attracts increasing attention as it delivers richer
information when tackling a target task from an unlabeled target domain by leveraging the …

Deep neural network has achieved promising results for automatic glaucoma detection on
fundus images. Nevertheless, the intrinsic discrepancy across glaucoma datasets is …