standard convolution that applies convolution to all channels let us take the same input volume F to understand this process F has a shape D F cross D F cross M where D F is the width and height of the input volume and M is the number of input channels like I mentioned before for depth wise convolution we use filters or kernels K of shape DK cross DK cross one here DK is the width and height of the square kernel and it has a depth of 1 because this convolution is only applied to a channel unlike standard convolution