In a convolutional neural network, what is the primary function of pooling layers?

Pooling layers play a critical role in convolutional neural networks (CNNs) primarily by reducing the dimensionality of the data while retaining essential features. This reduction in dimensionality helps to decrease the computational load on subsequent layers, which improves the efficiency and speed of the model. Additionally, by simplifying the data representation, pooling layers can help prevent overfitting by ensuring that the model does not become too complex and remains robust against noise or minor variations in the input data.

While enhancing feature extraction and normalization are vital functions within neural networks, these processes are more closely associated with convolutional layers and other techniques rather than pooling. Dropout is a regularization technique used to prevent overfitting, not a function of pooling layers. Therefore, the primary purpose of pooling layers in a CNN is indeed to reduce dimensionality, allowing for a more efficient processing of features while maintaining the most significant aspects of the input data.