What is the primary function of the pooling layer in a CNN?

The primary function of the pooling layer in a Convolutional Neural Network (CNN) is indeed to reduce the spatial dimensions of the feature map. Pooling helps to down-sample the feature maps produced by convolutional layers, effectively lowering the number of parameters and computation in the network. This reduction is beneficial as it helps in decreasing the risk of overfitting while retaining the most important information from the feature maps.

By condensing the spatial representation, pooling layers can also contribute to making the detection of features more robust to variations in scale and position, which is vital in tasks such as image recognition. Common pooling techniques include max pooling and average pooling, both of which yield smaller feature maps while preserving key features from the original input. This is why the response that highlights the pooling layer's role in reducing spatial dimensions is correct.