Howtosignalprocessing
- What is Depthwise separable convolution?
- What is the difference between Depthwise convolution and Depthwise separable convolution?
- What is the benefit of Depthwise separable convolution?
- How many parameters are in Depthwise separable convolution?
What is Depthwise separable convolution?
The depthwise separable convolution is so named because it deals not just with the spatial dimensions, but with the depth dimension — the number of channels — as well. An input image may have 3 channels: RGB. After a few convolutions, an image may have multiple channels.
What is the difference between Depthwise convolution and Depthwise separable convolution?
While standard convolution performs the channelwise and spatial-wise computation in one step, Depthwise Separable Convolution splits the computation into two steps: depthwise convolution applies a single convolutional filter per each input channel and pointwise convolution is used to create a linear combination of the ...
What is the benefit of Depthwise separable convolution?
Depthwise separable convolutions reduce the number of parameters and computa- tion used in convolutional operations while increasing representational efficiency.
How many parameters are in Depthwise separable convolution?
However, for a depthwise convolution, the operation applies to each channel separately, so the number of parameters is only (3 x 3 x 16) = 144.
