CNNs use pooling layers to reduce the size of the representation, to speed up the computation of the network, and to ensure robust feature extraction. The pooling layer is mostly stacked on top of the convolutional layer and this layer heavily downsizes the input dimension to reduce the computation in the network and also reduce overfitting.
There are two most commonly used types of pooling techniques :
- Max pooling: This type of pooling does downsampling by dividing the input matrix into pooling regions followed by computing the max values of each region.
Here's an example:
- Average pooling: This type of pooling does downsampling by dividing the input matrix into pooling regions followed by computing the average values of each region.
Here's an example:
In this recipe, we will learn how...
