Depwise separable filters.
describe the MobileNet structure and conclude with descriptions of the two model shrinking hyperparameters width multiplier and resolution multiplier
A standard convolution both filters and combines inputs into a new set of outputs in one step. The depthwise separable convolution splits this into two layers, a separate layer for filtering and a separate layer for combining. For MobileNets the depthwise convolution applies a single ﬁlter to each input channel. The pointwise convolution then applies a 1×1 convolution to combine the outputs the depthwise convolution.
A bottleneck layer is a layer that contains few nodes compared to the previous layers. It can be used to obtain a representation of the input with reduced dimensionality.