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Fast Fourier Convolution
2020
Neural Information Processing Systems
Vanilla convolutions in modern deep networks are known to operate locally and at fixed scale (e.g., the widely-adopted 3 × 3 kernels in image-oriented tasks). This causes low efficacy in connecting two distant locations in the network. In this work, we propose a novel convolutional operator dubbed as fast Fourier convolution (FFC), which has the main hallmarks of non-local receptive fields and cross-scale fusion within the convolutional unit. According to spectral convolution theorem in Fourier
dblp:conf/nips/ChiJM20
fatcat:wk7jkrrxuzarxfb3dlzo7rhw6y