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Fast Fourier Convolution - NIPS

Fast Fourier ConvolutionLu Chi1, Borui Jiang2, Yadong Mu1 1 Wangxuan Institute of Computer Technology,2 Center for Data SciencePeking convolutions in modern deep networks are known to operate locally and atfixed scale ( , the widely-adopted3 3kernels in image-oriented tasks). Thiscauses low efficacy in connecting two distant locations in the network. In this work,we propose a novel convolutional operator dubbed asfast Fourier Convolution (FFC), which has the main hallmarks of non-local receptive fields and cross-scalefusion within the convolutional unit. According to spectral Convolution theorem inFourier theory, point-wise update in the spectral domain globally affects all inputfeatures involved in Fourier transform , which sheds light on neural architecturaldesign with non-local receptive field.

convolution to the full resolution of input feature map in an efficient way. We adopt discrete Fourier transform (DFT) for this purpose, using the accelerated version with Cooley-Tukey algorithm [8]. Figure 1 depicts our proposed spectral transformer. Inspired by the bottleneck block in …

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