Symbol N

The passage around this formula

…convolution splits that into a depthwise layer that filters each input channel on its own, followed by a 1×1 pointwise convolution that recombines the results [ 1 ] . For a $D_K$ $\times$ $D_K$ kernel, M input channels, N output channels and a $D_F$ $\times$ $D_F$ feature map, a standard convolution costs $D_K$ $\cdot$ $D_K$ $\cdot$ M $\cdot$ N $\cdot$ $D_F$ $\cdot$ $D_F$ multiply-adds. The paper gives the depthwise separable replacement’s cost, and its ratio to the standard cost, directly: $\frac{D_K \cdot D_K \cdot M \cdot D_F \cdot D_F + M \cdot N \cdot D_F \cdot D_F}{D_K \cdot D_K \cdot M \cdot N \cdot D_F \cdot D_F} = \frac{1}{N} + \frac{1}{D_K^2}$. With the near-universal…

Read this part in the article →