Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Unsupervised Domain adaptation methods solve the adaptation problem for an unlabeled
target set, assuming that the source dataset is available with all labels. However, the …

Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target
data set by only using a pre-trained source model. However, the absence of the source data …

Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from multiple
labeled source domains to an unlabeled target domain. Although current methods achieve …

Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source
domain to an unlabeled target domain. Existing UDA-based semantic segmentation …

We present an approach for unsupervised domain adaptation {—} with a strong focus on
practical considerations of within-domain class imbalance and between-domain class …

Accurate breast mass segmentation of automated breast ultrasound (ABUS) images plays a
crucial role in 3D breast reconstruction which can assist radiologists in surgery planning …

Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain
to a new target domain with only unlabeled data. Most existing methods suffer from …

With the development of deep neural networks, the performance of crowd counting and pixel-
wise density estimation is continually being refreshed. Despite this, there are still two …

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