A prevailing problem in many machine learning tasks is that the training (ie, source domain)
and test data (ie, target domain) have different distribution [ie, non-independent identical …

Abstract Standard Unsupervised Domain Adaptation (UDA) methods assume the availability
of both source and target data during the adaptation. In this work, we investigate Source-free …

In unsupervised domain adaptation (UDA), a target-domain model is trained by the
supervised knowledge from a source domain. Although UDA has recently received much …

Unsupervised domain adaptation (UDA) methods for learning domain invariant
representations have achieved remarkable progress. However, most of the studies were …

Abstract Unsupervised Domain Adaptation (UDA) aims to align the labeled source
distribution with the unlabeled target distribution to obtain domain invariant predictive …

Unsupervised domain adaptation (UDA) has recently become an appealing research topic
in visual recognition, since it exploits all accessible well-labeled source data to train a model …

For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many
approaches align the source and target domains in the feature space by adversarial …

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

Unsupervised domain adaptation (UDA) enables a learning machine to adapt from a
labeled source domain to an unlabeled target domain under the distribution shift. Thanks to …

This work focuses on a practical knowledge transfer task defined as Source-Free
Unsupervised Domain Adaptation (SFUDA), where only a well-trained source model and …