What does the max-pooling operation do in a CNN?

The max-pooling operation in a Convolutional Neural Network (CNN) primarily serves to reduce the dimensionality of the feature maps while retaining the most important information. By taking the maximum value from a selected region of the input data, max-pooling effectively down-samples the feature map.

Max-pooling operates by sliding a window over the input feature map, and for each window position, it identifies and outputs the maximum value. This process not only reduces the size of the data being processed in subsequent layers (minimizing the amount of computation required) but also helps to make the feature representations more invariant to small translations in the input. Keeping only the maximum values ensures that the most prominent features are preserved while reducing the overall data representation size.

The other options are related but don't accurately describe the primary function of max-pooling. While it does return maximum values from the input, focusing on that alone misses the crucial aspect of dimensionality reduction, which is a key characteristic of the max-pooling operation in the context of CNNs.