Pooling layers in convolutional neural networks (CNNs) are used to reduce the spatial dimensions (width and height) of the input volume for the next convolutional layer. They work by summarizing the features present in regions of the input.
Function of Pooling Layers
The most common form of pooling is max pooling, where the maximum element from the region of the input is selected. Pooling helps to reduce computation, control overfitting by providing an abstracted form of the representation, and makes the detection of features invariant to scale and orientation changes.
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