What is the primary purpose of a pooling layer in a convolutional neural network (CNN)?

The primary purpose of a pooling layer in a convolutional neural network (CNN) is to reduce the size of the feature maps, thereby simplifying the computations needed for subsequent layers. This dimensionality reduction helps to achieve several key goals: it decreases the number of parameters and computations in the network, which can help to mitigate overfitting, and it also provides a form of translation invariance. By summarizing the information in the feature maps (for instance, by taking the maximum or average of a set of features), pooling layers condense the spatial size while retaining the most significant features that are essential for the classification or detection tasks the CNN is being trained for.

In contrast, other options do not accurately reflect the role of pooling layers. Increasing the size of feature maps is counterproductive, as pooling is designed to do the opposite. Enhancing the detail of feature maps also runs contrary to the purpose of pooling, which focuses on summarizing information rather than adding detail. Finally, combining multiple models into one relates to a different concept in machine learning, such as model ensemble techniques, and is not related to the function of pooling layers in CNNs.