In this study, we investigate a challenging problem, namely, robust domain adaptation,
where data from only a single well-labeled source domain are available in the training …

Abstract Domain adaptation aims to exploit the knowledge in source domain to promote the
learning tasks in target domain, which plays a critical role in real-world applications …

Domain adaptation aims to leverage the supervision signal of source domain to obtain an
accurate model for target domain, where the labels are not available. To leverage and adapt …

An essential problem in domain adaptation is to understand and make use of distribution
changes across domains. For this purpose, we first propose a flexible Generative Domain …

Most efforts in unsupervised domain adaptation (UDA) focus on learning the domain-
invariant representations between the two domains. However, such representations may still …

Domain adaptation aims to facilitate the learning task in an unlabeled target domain by
leveraging the auxiliary knowledge in a well-labeled source domain from a different …

Abstract Domain adaption aims to promote the learning tasks in target domain by using the
knowledge from source domain whose data distribution is different from target domain. The …

Abstract Domain adaptation has been widely explored by transferring the knowledge from a
label-rich source domain to a related but unlabeled target domain. Most existing domain …

Multi-source domain adaptation has attracted great attention in machine learning
community. Most of these methods focus on weighting the predictions produced by the …

Beyond classical domain-specific adversarial training, a recently proposed task-specific
framework has achieved a great success in single source domain adaptation by utilizing …