Max pooling is a down-sampling technique used in Convolutional Neural Networks (CNNs) to reduce the spatial dimensions of feature maps while retaining the most important information. By selecting the maximum value from a defined region (or 'pool') of the input feature map, max pooling helps to capture dominant features and reduce the computational load for subsequent layers, leading to improved efficiency and robustness in image analysis tasks.
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