Data quantity and quality are crucial factors for data-driven learning methods. In some target
problem domains, there are not many data samples available, which could significantly …

Generalizing deep neural networks to new target domains is critical to their real-world utility.
In practice, it may be feasible to get some target data labeled, but to be cost-effective it is …

Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under
unsupervised or few-shot semi-supervised settings, recently the solution of active learning …

We introduce the problem of domain adaptation under Open Set Label Shift (OSLS), where
the label distribution can change arbitrarily and a new class may arrive during deployment …

Abstract Conventional Unsupervised Domain Adaptation (UDA) methods presume source
and target domain data to be simultaneously available during training. Such an assumption …

Universal domain adaptation aims to transfer the knowledge of common classes from the
source domain to the target domain without any prior knowledge on the label set, which …

Active domain adaptation (ADA) aims to improve the model adaptation performance by
incorporating the active learning (AL) techniques to label a maximally-informative subset of …

To ease the burden of labeling, unsupervised domain adaptation (UDA) aims to transfer
knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset …

Abstract Domain adaptation (DA) aims to transfer knowledge from a label-rich but
heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and …

Unsupervised domain adaptation has attracted appealing academic attentions by
transferring knowledge from labeled source domain to unlabeled target domain. However …