  Recognition across domains has recently become an active topic in the
research community. However, it has been largely overlooked in the problem of
recognition in new unseen domains. Under this condition, the delivered deep
network models are unable to be updated, adapted, or fine-tuned. Therefore,
recent deep learning techniques, such as domain adaptation, feature
transferring, and fine-tuning, cannot be applied. This paper presents a novel
approach to the problem of domain generalization in the context of deep
learning. The proposed method is evaluated on different datasets in various
problems, i.e. (i) digit recognition on MNIST, SVHN, and MNIST-M, (ii) face
recognition on Extended Yale-B, CMU-PIE and CMU-MPIE, and (iii) pedestrian
recognition on RGB and Thermal image datasets. The experimental results show
that our proposed method consistently improves performance accuracy. It can
also be easily incorporated with any other CNN frameworks within an end-to-end
deep network design for object detection and recognition problems to improve
their performance.
