  The convolutional neural network (CNN) remains an essential tool in solving
computer vision problems. Standard convolutional architectures consist of
stacked layers of operations that progressively downscale the image. Aliasing
is a well-known side-effect of downsampling that may take place: it causes
high-frequency components of the original signal to become indistinguishable
from its low-frequency components. While downsampling takes place in the
max-pooling layers or in the strided-convolutions in these models, there is no
explicit mechanism that prevents aliasing from taking place in these layers.
Due to the impressive performance of these models, it is natural to suspect
that they, somehow, implicitly deal with this distortion. The question we aim
to answer in this paper is simply: "how and to what extent do CNNs counteract
aliasing?" We explore the question by means of two examples: In the first, we
assess the CNNs capability of distinguishing oscillations at the input, showing
that the redundancies in the intermediate channels play an important role in
succeeding at the task; In the second, we show that an image classifier CNN
while, in principle, capable of implementing anti-aliasing filters, does not
prevent aliasing from taking place in the intermediate layers.
