What is the purpose of pooling in CNNs?

Pooling in convolutional neural networks (CNNs) serves several important purposes primarily related to down-sampling the feature maps produced by convolutional layers. The main goal of pooling is to reduce the spatial dimensions of the feature maps, which subsequently decreases the computational load and complexity of the model, helping it to generalize better by providing a form of translation invariance.

By simplifying the representation of the data, pooling helps maintain the most essential information while discarding less critical details, thus making the model more efficient. This down-sampling process does not blur images for filter alignment; rather, it retains significant features, allowing the network to learn more robust features for classification or detection tasks.

While blurring can be a side effect in some image processing techniques, it is not the intent of pooling layers in CNNs. Instead, pooling specifically focuses on summarizing the local features of the region being pooled, which is typically accomplished through techniques such as max pooling or average pooling. These methods enable the model to keep only the strongest features while removing minor details that do not significantly contribute to the task at hand.