  As the saying goes, sometimes less is more -- and when it comes to neural
networks, that couldn't be more true. Enter pruning, the art of selectively
trimming away unnecessary parts of a network to create a more streamlined,
efficient architecture. In this paper, we introduce a novel end-to-end pipeline
for model pruning via the frequency domain. This work aims to shed light on the
interoperability of intermediate model outputs and their significance beyond
the spatial domain. Our method, dubbed Common Frequency Domain Pruning (CFDP)
aims to extrapolate common frequency characteristics defined over the feature
maps to rank the individual channels of a layer based on their level of
importance in learning the representation. By harnessing the power of CFDP, we
have achieved state-of-the-art results on CIFAR-10 with GoogLeNet reaching an
accuracy of 95.25%, that is, +0.2% from the original model. We also outperform
all benchmarks and match the original model's performance on ImageNet, using
only 55% of the trainable parameters and 60% of the FLOPs. In addition to
notable performances, models produced via CFDP exhibit robustness to a variety
of configurations including pruning from untrained neural architectures, and
resistance to adversarial attacks. The implementation code can be found at
https://github.com/Skhaki18/CFDP.
