  We focus on building robustness in the convolutions of neural visual
classifiers, especially against natural perturbations like elastic
deformations, occlusions and Gaussian noise. Existing CNNs show outstanding
performance on clean images, but fail to tackle naturally occurring
perturbations. In this paper, we start from elastic perturbations, which
approximate (local) view-point changes of the object. We present
elastically-augmented convolutions (EAConv) by parameterizing filters as a
combination of fixed elastically-perturbed bases functions and trainable
weights for the purpose of integrating unseen viewpoints in the CNN. We show on
CIFAR-10 and STL-10 datasets that the general robustness of our method on
unseen occlusion, zoom, rotation, image cut and Gaussian perturbations
improves, while significantly improving the performance on clean images without
any data augmentation.
