Depthwise separable convolutions

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 output of the depthwise convolution.

Related ideas are often used to reduce the size/complexity of convolutional layers. It reduces expressivity of convolutions but its less parameters. For example Exploiting Redundancy - Separable Group Convolutional Networks on Lie Groups

Also used in (ConvNext) A ConvNet for the 2020s