Depthwise Separable Convolutions

Hard

neural-networks Google Apple Tesla

Multiple Choice

It splits the operation into a per-channel depthwise convolution and a 1x1 pointwise convolution, reducing cost by approximately 1/k^2 where k is kernel size It applies convolution only to even-indexed channels, halving the computation It replaces spatial convolution with frequency-domain multiplication, reducing cost from O(n^2) to O(n log n) It uses shared weights across all channels, reducing parameters to 1/C of a standard convolution

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