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.

翻译：常言道，有时少即是多——而对于神经网络而言，这句话尤为贴切。剪枝技术应运而生，它是一门通过选择性修剪网络中不必要的部分来构建更精简、高效架构的艺术。本文提出了一种全新的基于频域的端到端模型剪枝流水线。本研究旨在阐明中间模型输出的可解释性及其在空间域之外的重要性。我们提出的方法——公共频域剪枝（CFDP），旨在通过特征图推断公共频域特征，并据此对各层通道在学习表征中的重要程度进行排序。借助CFDP的强大功能，我们在CIFAR-10数据集上使用GoogLeNet取得了最先进的结果，准确率达到95.25%，相比原始模型提升了0.2%。此外，我们仅使用55%的可训练参数和60%的浮点运算量（FLOPs），便在ImageNet数据集上超越了所有基准测试，并匹配了原始模型的性能。除了卓越的性能表现，通过CFDP生成的模型还展现出对多种配置的鲁棒性，包括对未训练神经架构的剪枝，以及对对抗攻击的抵抗力。实现代码详见https://github.com/Skhaki18/CFDP。