  We investigate the impact of aliasing on generalization in Deep Convolutional
Networks and show that data augmentation schemes alone are unable to prevent it
due to structural limitations in widely used architectures. Drawing insights
from frequency analysis theory, we take a closer look at ResNet and
EfficientNet architectures and review the trade-off between aliasing and
information loss in each of their major components. We show how to mitigate
aliasing by inserting non-trainable low-pass filters at key locations,
particularly where networks lack the capacity to learn them. These simple
architectural changes lead to substantial improvements in generalization on
i.i.d. and even more on out-of-distribution conditions, such as image
classification under natural corruptions on ImageNet-C [11] and few-shot
learning on Meta-Dataset [26]. State-of-the art results are achieved on both
datasets without introducing additional trainable parameters and using the
default hyper-parameters of open source codebases.
