The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source
domains to an unlabeled target domain. MDA is a challenging task due to the severe …

Recently, extensive researches have been proposed to address the UDA problem, which
aims to learn transferrable models for the unlabeled target domain. Among them, the optimal …

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled
source domain to an unlabeled target domain. Typically, to guarantee desirable knowledge …

Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for
the target domain, in which only unlabeled and a small number of labeled data are …

Abstract Existing Optimal Transport (OT) methods mainly derive the optimal transport
plan/matching under the criterion of transport cost/distance minimization, which may cause …

Multi-source domain adaptation (DA) is more challenging than conventional DA because the
knowledge is transferred from several source domains to a target domain. To this end, we …

Domain adaptation leverages the knowledge in one domain-the source domain-to improve
learning efficiency in another domain-the target domain. Existing heterogeneous domain …

The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to
train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner …

Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target
domain by borrowing knowledge from a heterogeneous source domain. In this paper, we …