Abstract Existing Unsupervised Domain Adaptation (UDA) literature adopts the covariate
shift and conditional shift assumptions, which essentially encourage models to learn …

Unsupervised domain adaptive classifcation intends to improve the classifcation
performance on unlabeled target domain. To alleviate the adverse effect of domain shift …

Abstract Universal Semi-Supervised Learning (UniSSL) aims to solve the open-set problem
where both the class distribution (ie, class set) and feature distribution (ie, feature domain) …

Domain generalization (DG) aims to learn a model on one or more different but related
source domains that could be generalized into an unseen target domain. Existing DG …

Unsupervised domain adaptation for intelligent fault diagnosis requires a well-annotated
source domain to transfer knowledge to an unlabeled target domain, but the ubiquitous …

Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled
target domain supervised by a black-box predictor trained on a source domain. It does not …

Unsupervised domain adaptation (UDA) tries to overcome the need of a large labeled
dataset by transferring knowledge from a source dataset, with lots of labeled data, to a target …

Multi-source domain adaptation has been intensively studied. The distribution shift in
features inherent to specific domains causes the negative transfer problem, degrading a …

Domain adaptation is concerned with the problem of generalizing a classification model to a
target domain with little or no labeled data, by leveraging the abundant labeled data from a …

Multi-source domain adaptation utilizes multiple source domains to learn the knowledge and
transfers it to an unlabeled target domain. To address the problem, most of the existing …