  We address the problem of camera pose estimation in visual localization.
Current regression-based methods for pose estimation are trained and evaluated
scene-wise. They depend on the coordinate frame of the training dataset and
show a low generalization across scenes and datasets. We identify the dataset
shift an important barrier to generalization and consider transfer learning as
an alternative way towards a better reuse of pose estimation models. We revise
domain adaptation techniques for classification and extend them to camera pose
estimation, which is a multi-regression task. We develop a deep adaptation
network for learning scene-invariant image representations and use adversarial
learning to generate such representations for model transfer. We enrich the
network with self-supervised learning and use the adaptability theory to
validate the existence of scene-invariant representation of images in two given
scenes. We evaluate our network on two public datasets, Cambridge Landmarks and
7Scene, demonstrate its superiority over several baselines and compare to the
state of the art methods.
