Recent advances in domain adaptation show that deep self-training presents a powerful
means for unsupervised domain adaptation. These methods often involve an iterative …

Adversarial domain adaptation has made remarkable advances in learning transferable
representations for knowledge transfer across domains. While adversarial learning …

Given labeled instances on a source domain and unlabeled ones on a target domain,
unsupervised domain adaptation aims to learn a task classifier that can well classify target …

Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where
there is a single, labeled, source and a single target domain. However, in many real-world …

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multi-source
domains to one target domain. Inspired by single-source domain adaptation, existing …

Abstract Domain adaptation (DA) is the topical problem of adapting models from labelled
source datasets so that they perform well on target datasets where only unlabelled or …

In the field of computer vision, without sufficient labeled images, it is challenging to train an
accurate model. However, through visual adaptation from source to target domains, a …

Most unsupervised domain adaptation (UDA) methods assume that labeled source images
are available during model adaptation. However, this assumption is often infeasible owing to …

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

Domain adaptation is proposed to deal with the challenging problem where the probability
distribution of the training source is different from the testing target. Recently, adversarial …