We propose a manifold alignment based approach for heterogeneous domain adaptation. A
key aspect of this approach is to construct mappings to link different feature spaces in order …

Unsupervised domain adaptation is effective in leveraging rich information from a labeled
source domain to an unlabeled target domain. Though deep learning and adversarial …

Domain adaptation (DA) aims to accomplish tasks on unlabeled target data by learning and
transferring knowledge from related source domains. In order to learn a discriminative and …

In this chapter, we present CORrelation ALignment (CORAL), a simple yet effective method
for unsupervised domain adaptation. CORAL minimizes domain shift by aligning the second …

While domain adaptation (DA) aims to associate the learning tasks across data domains,
heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain …

Domain adaptation methods aim to learn a good prediction model in a label-scarce target
domain by leveraging labeled patterns from a related source domain where there is a large …

Transferring knowledge from a source domain to a target domain by domain adaptation has
been an interesting and challenging problem in many machine learning applications. The …

Deep neural networks, trained with large amount of labeled data, can fail to generalize well
when tested with examples from a target domain whose distribution differs from the training …

Currently, unsupervised heterogeneous domain adaptation in a generalized setting, which
is the most common scenario in real-world applications, is under insufficient exploration …

Abstract Domain adaptation generalizes a learning model across source domain and target
domain that follow different distributions. Most existing work follows a two-step procedure …