  Deep Convolutional Neural Networks (CNNs) are empirically known to be
invariant to moderate translation but not to rotation in image classification.
This paper proposes a deep CNN model, called CyCNN, which exploits polar
mapping of input images to convert rotation to translation. To deal with the
cylindrical property of the polar coordinates, we replace convolution layers in
conventional CNNs to cylindrical convolutional (CyConv) layers. A CyConv layer
exploits the cylindrically sliding windows (CSW) mechanism that vertically
extends the input-image receptive fields of boundary units in a convolutional
layer. We evaluate CyCNN and conventional CNN models for classification tasks
on rotated MNIST, CIFAR-10, and SVHN datasets. We show that if there is no data
augmentation during training, CyCNN significantly improves classification
accuracies when compared to conventional CNN models. Our implementation of
CyCNN is publicly available on https://github.com/mcrl/CyCNN.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing