What is a primary advantage of pooling layers in CNNs?

Pooling layers in Convolutional Neural Networks (CNNs) play a crucial role in simplifying the information processed by the network, and one of their primary advantages is indeed related to reducing the computational load. By down-sampling the input feature maps after convolutional layers, pooling layers effectively decrease the spatial dimensions of the data. This reduction not only leads to fewer parameters and computations in subsequent layers but also helps mitigate overfitting. Consequently, the model can operate more efficiently, requiring less memory and processing power, which is essential when dealing with large datasets or real-time applications.

The other options present concepts that do not align with the primary function of pooling layers. For instance, increasing dimensionality is contrary to the purpose of pooling, which is to condense information. Retaining all pixel information is also not a function of pooling, as pooling creates summaries of the input, losing some detailed pixel data in the process. Lastly, the sharing of weights pertains more to convolutional layers rather than pooling layers, which primarily perform operations like max or average pooling without weight sharing.