  Generalization of machine learning models trained on a set of source domains
on unseen target domains with different statistics, is a challenging problem.
While many approaches have been proposed to solve this problem, they only
utilize source data during training but do not take advantage of the fact that
a single target example is available at the time of inference. Motivated by
this, we propose a method that effectively uses the target sample during
inference beyond mere classification. Our method has three components - (i) A
label-preserving feature or metric transformation on source data such that the
source samples are clustered in accordance with their class irrespective of
their domain (ii) A generative model trained on the these features (iii) A
label-preserving projection of the target point on the source-feature manifold
during inference via solving an optimization problem on the input space of the
generative model using the learned metric. Finally, the projected target is
used in the classifier. Since the projected target feature comes from the
source manifold and has the same label as the real target by design, the
classifier is expected to perform better on it than the true target. We
demonstrate that our method outperforms the state-of-the-art Domain
Generalization methods on multiple datasets and tasks.
