  Most visual recognition methods implicitly assume the data distribution
remains unchanged from training to testing. However, in practice domain shift
often exists, where real-world factors such as lighting and sensor type change
between train and test, and classifiers do not generalise from source to target
domains. It is impractical to train separate models for all possible situations
because collecting and labelling the data is expensive. Domain adaptation
algorithms aim to ameliorate domain shift, allowing a model trained on a source
to perform well on a different target domain. However, even for the setting of
unsupervised domain adaptation, where the target domain is unlabelled,
collecting data for every possible target domain is still costly. In this
paper, we propose a new domain adaptation method that has no need to access
either data or labels of the target domain when it can be described by a
parametrised vector and there exits several related source domains within the
same parametric space. It greatly reduces the burden of data collection and
annotation, and our experiments show some promising results.
