XSepConv: Extremely Separated Convolution
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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Methods used in the Paper
| METHOD | TYPE | |
|---|---|---|
Depthwise Convolution
|
Convolutions | |
Convolution
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Convolutions |