  Deep convolutional networks have become the mainstream in computer vision
applications. Although CNNs have been successful in many computer vision tasks,
it is not free from drawbacks. The performance of CNN is dramatically degraded
by geometric transformation, such as large rotations. In this paper, we propose
a novel CNN architecture that can improve the robustness against geometric
transformations without modifying the existing backbones of their CNNs. The key
is to enclose the existing backbone with a geometric transformation (and the
corresponding reverse transformation) and a feature map ensemble. The proposed
method can inherit the strengths of existing CNNs that have been presented so
far. Furthermore, the proposed method can be employed in combination with
state-of-the-art data augmentation algorithms to improve their performance. We
demonstrate the effectiveness of the proposed method using standard datasets
such as CIFAR, CUB-200, and Mnist-rot-12k.
