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

The primary function of a pooling layer in a convolutional neural network (CNN) is indeed to reduce computation time and maintain the most significant features by downsampling the output from the previous convolutional layer. Pooling accomplishes this by condensing the spatial dimensions of the input, usually by selecting the maximum value within a specified window (as seen in max pooling) or calculating the average value (as seen in average pooling).

This process not only reduces the size of the data that needs to be processed in subsequent layers, but it also helps make the network more robust by providing a form of translational invariance. That is, it allows the model to be less sensitive to the exact position of features in the input data, focusing instead on the presence of the features themselves.

The other choices do not accurately describe the primary purpose of a pooling layer. While retaining important information is relevant, the mechanism of zero-padding is typically related to convolution operations rather than pooling. Preparing data for a fully connected layer involves flattening, but this is not the main role of the pooling layer. Finally, while pooling does involve downsampling, it does not specifically aim to increase the distance between filters; rather, it simplifies the feature maps generated by the convolutional layers.