Pooling In Convolutional Neural Networks

Pooling in CNNs reduces image dimensions by systematically compressing feature maps, helping networks recognize important patterns while reducing computational load. Max pooling selects the highest value in each filter region to create sharper representations, while average pooling uses mean values for smoother outputs, both enabling translation invariance and preventing overfitting. The 2×2 pooling operations with stride=2 produce images half the original size, maintaining essential features even after multiple iterations—allowing CNNs to efficiently process complex visual data.