  We show how parameter redundancy in Convolutional Neural Network (CNN)
filters can be effectively reduced by pruning in spectral domain. Specifically,
the representation extracted via Discrete Cosine Transform (DCT) is more
conducive for pruning than the original space. By relying on a combination of
weight tensor reshaping and reordering we achieve high levels of layer
compression with just minor accuracy loss. Our approach is applied to compress
pretrained CNNs and we show that minor additional fine-tuning allows our method
to recover the original model performance after a significant parameter
reduction. We validate our approach on ResNet-50 and MobileNet-V2 architectures
for ImageNet classification task.
