You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: The standard convolution operation has the effect of filtering features based on theconvolutional kernels and combining features in order to produce a new representation.The filtering and combination steps can be split into two steps via the use offactorized convolutions called depthwise separable convolutions for substantial reduction in computational cost. Depthwise separable convolution are made up of two layers: depthwise convolutions and pointwise convolutions.We use depthwise convolutions to apply a single filter per each input channel (input depth). Pointwise convolution, asimple 1\times 1 convolution, is then used to create a linear combination of the output of the depthwise layer. MobileNets use bothbatchnorm and ReLU nonlinearities for both layers. Depthwise convolution is extremely efficient relative to standard convolution. However it only filters input channels, it does not combine them to create new features. So an additional layer that computes a linear combination of the output of depthwise convolution via 1\times 1 convolution is needed in order to generate these new features. The combination of depthwise convolution and 1\times 1 (pointwise) convolution is called depthwise separable convolution which was originally introduced in [26]. The MobileNet model is based on depthwise separable convolutions which is a form of factorized convolutions which factorize a standard convolution into a depthwise convolution and a 1\times 1 convolution called a pointwise convolution. For MobileNets the depthwise convolution applies a single filter to each input channel. The pointwise convolution then applies a 1\times 1 convolution to combine the outputs the depthwise convolution. 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. This factorization has the effect of drastically reducing computation and model size. Figure 2 shows how a standard convolution 2(a) is factorized into a depthwise convolution 2(b) and a 1\times 1 pointwise convolution 2(c).

Question: What is a depthwise separable convolution means?
Depthwise separable convolution is made up of two layers: depthwise convolutions and pointwise convolutions where depthwise convolutions apply a single filter per each input channel and a Pointwise convolution creates a linear combination of the output of the depthwise layer.