Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but
different well-labeled source domain to a new unlabeled target domain. Most existing UDA …

Open-set domain adaptation is a develo** and practical solution to training deep networks
using unlabeled data which have been collected among unknown data and are under …

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 …

Deep domain adaptation methods have achieved appealing performance by learning
transferable representations from a well-labeled source domain to a different but related …

This work addresses the unsupervised domain adaptation problem, especially in the case of
class labels in the target domain being only a subset of those in the source domain. Such a …

Due to the unavailability of labeled target data, most existing unsupervised domain
adaptation (UDA) methods alternately classify the unlabeled target samples and discover a …

Existing domain adaptation approaches often try to reduce distribution difference between
source and target domains and respect domain-specific discriminative structures by some …

Domain adaptation (DA) attempts to transfer knowledge learned in the labeled source
domain to the unlabeled but related target domain without requiring large amounts of target …

Conventional domain adaptation methods usually resort to deep neural networks or
subspace learning to find invariant representations across domains. However, most deep …