XSepConv: Extremely Separated Convolution

27 Feb 2020 Jiarong Chen Zongqing Lu Jing-Hao Xue Qingmin Liao

Depthwise convolution has gradually become an indispensable operation for modern efficient neural networks and larger kernel sizes ($\ge5$) have been applied to it recently. In this paper, we propose a novel extremely separated convolutional block (XSepConv), which fuses spatially separable convolutions into depthwise convolution to further reduce both the computational cost and parameter size of large kernels... (read more)

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