  Overfitting is a problem in Convolutional Neural Networks (CNN) that causes
poor generalization of models on unseen data. To remediate this problem, many
new and diverse data augmentation methods (DA) have been proposed to supplement
or generate more training data, and thereby increase its quality. In this work,
we propose a new data augmentation algorithm: VoronoiPatches (VP). We primarily
utilize non-linear recombination of information within an image, fragmenting
and occluding small information patches. Unlike other DA methods, VP uses small
convex polygon-shaped patches in a random layout to transport information
around within an image. Sudden transitions created between patches and the
original image can, optionally, be smoothed. In our experiments, VP
outperformed current DA methods regarding model variance and overfitting
tendencies. We demonstrate data augmentation utilizing non-linear
re-combination of information within images, and non-orthogonal shapes and
structures improves CNN model robustness on unseen data.
