Pooling

Pooling Layers is a technique used in convolutional neural networks to reduce the spatial dimensions of feature maps by calculating the average value within a defined window (or region). It effectively downsamples the input while preserving the general characteristics of the data, acting as a smoothing operation

Pooling serves multiple purposes: reducing computational load, controlling overfitting, and creating hierarchical feature representations.

Max Pooling

Max pooling selects the maximum value from each region preserving the strongest feature responses while providing translation invariance and computational efficiency.

Average Pooling

Average pooling computes the mean, providing a smoother downsampling but potentially losing important peak responses.