Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain
configurations, including closed-set and partial-set DA, as well as multi-source and multi …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

Universal domain adaptation (UniDA) transfers knowledge between domains without any
constraint on the label sets, extending the applicability of domain adaptation in the wild. In …

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 …

Abstract Domain adaptation (DA) paves the way for label annotation and dataset bias issues
by the knowledge transfer from a label-rich source domain to a related but unlabeled target …

Deep neural networks (DNNs) excel at learning representations when trained on large-scale
datasets. Pre-trained DNNs also show strong transferability when fine-tuned to other labeled …

A common strategy adopted by existing state-of-the-art unsupervised domain adaptation
(UDA) methods is to employ two classifiers to identify the misaligned local regions between …

In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach
with the inherent conditional and label shifts, in which we aim to align the distributions wrt …