Pooling layers in convolutional neural networks (CNNs) are used to reduce the spatial dimensions of the input data while retaining important features. They help reduce computational load, memory usage, and overfitting by downsampling the feature maps produced by convolutional layers. Common pooling techniques include max pooling and average pooling, where max pooling selects the maximum value from a region, and average pooling computes the average.
By using pooling layers, CNNs can learn hierarchical feature representations at different scales, allowing the network to focus on the most significant patterns. Pooling also helps the network become more invariant to small translations or distortions in the input data.
Overall, pooling layers are crucial for improving the efficiency and generalization of CNNs in tasks like image classification and object detection.