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This document outlines a method called Octave Convolution that can reduce spatial redundancy in convolutional neural networks. It separates low and high spatial frequency signals by applying convolutions to alternatingly downsampled feature maps. Octave Convolution was tested on ImageNet where it achieved comparable accuracy to vanilla convolutions while using fewer computations.