  Deep learning models have a large number of free parameters that must be
estimated by efficient training of the models on a large number of training
data samples to increase their generalization performance. In real-world
applications, the data available to train these networks is often limited or
imbalanced. We propose a sampling method based on the radial transform in a
polar coordinate system for image augmentation to facilitate the training of
deep learning models from limited source data. This pixel-wise transform
provides representations of the original image in the polar coordinate system
by generating a new image from each pixel. This technique can generate radial
transformed images up to the number of pixels in the original image to increase
the diversity of poorly represented image classes. Our experiments show
improved generalization performance in training deep convolutional neural
networks with radial transformed images.
