2.1.3. Pooling Layer

The pooling layer is also one of the most common and basic mechanisms of convolutional neural networks. It is actually a form of downsampling, and there are many forms of nonlinear pooling functions in convolutional neural networks. Max pooling function is the most common one. The principle of this mechanism is that when a feature of data is discovered, its exact location is far less important than its relative location with other features. Pooling reduces the size of the data space by constantly reducing the number of network parameters and the amount of computation. Overfitting can also be suppressed to some extent. The max-pooling operation can be expressed as shown in Figure 3:

The expression is (3):

$$a^k\_{\left(nh,nw,\varepsilon\right)} = \max\left(a^{k-1}\_{\left(nh\times strdrid::nh\times strdrid\times f\cdot\mu w\times strdrid::nw\times strdrid\times f\cdot\varepsilon\right)}\right) \tag{3}$$

where *nh* represents the height in the current pixel, *nw* represents the width of the current pixel, and *c* represents the channel, *f* represents the size of the pooling core, and *stride* represents the step size of the pooling core movement.

**Figure 3.** Maximum pooling operation.
