What is maximum pool?

A maximum pool is a type of pooling layer in a convolutional neural network (CNN). It is used to reduce the size of the input feature map and extract the most prominent and relevant features.

In a maximum pool layer, the input feature map is divided into non-overlapping regions (usually 2x2 or 3x3) and the maximum value within each region is selected as the output. Hence, the output feature map has a smaller size than the input feature map, which can reduce the computational cost and overfitting.

The maximum pool layer is a popular choice as it is simple yet effective in capturing the most important features in the input data. It is commonly used in various CNN architectures, such as AlexNet, VGGNet, and ResNet.