  Convolutional layers in CNNs implement linear filters which decompose the
input into different frequency bands. However, most modern architectures
neglect standard principles of filter design when optimizing their model
choices regarding the size and shape of the convolutional kernel. In this work,
we consider the well-known problem of spectral leakage caused by windowing
artifacts in filtering operations in the context of CNNs. We show that the
small size of CNN kernels make them susceptible to spectral leakage, which may
induce performance-degrading artifacts. To address this issue, we propose the
use of larger kernel sizes along with the Hamming window function to alleviate
leakage in CNN architectures. We demonstrate improved classification accuracy
over baselines with conventional $3\times 3$ kernels, on multiple benchmark
datasets including Fashion-MNIST, CIFAR-10, CIFAR-100 and ImageNet, via the
simple use of a standard window function in convolutional layers. Finally, we
show that CNNs employing the Hamming window display increased robustness
against certain types of adversarial attacks.
