  Domain adaptation approaches aim to exploit useful information from the
source domain where supervised learning examples are easier to obtain to
address a learning problem in the target domain where there is no or limited
availability of such examples. In classification problems, domain adaptation
has been studied under varying supervised, unsupervised and semi-supervised
conditions. However, a common situation when the labelled samples are available
for a subset of target domain classes has been overlooked. In this paper, we
formulate this particular domain adaptation problem within a generalized
zero-shot learning framework by treating the labelled source domain samples as
semantic representations for zero-shot learning. For this particular problem,
neither conventional domain adaptation approaches nor zero-shot learning
algorithms directly apply. To address this generalized zero-shot domain
adaptation problem, we present a novel Coupled Conditional Variational
Autoencoder (CCVAE) which can generate synthetic target domain features for
unseen classes from their source domain counterparts. Extensive experiments
have been conducted on three domain adaptation datasets including a bespoke
X-ray security checkpoint dataset to simulate a real-world application in
aviation security. The results demonstrate the effectiveness of our proposed
approach both against established benchmarks and in terms of real-world
applicability.
