Pooling Layers
In Convolutional Neural Networks (CNNs), pooling layers are used to reduce the spatial dimensions (width and height) of the input volume, while keeping the depth constant. Pooling layers operate independently on each depth slice of the input and downsample the spatial dimensions by taking the maximum, average, or other operation of a local neighborhood of the input. The main purpose of pooling layers is to progressively reduce the spatial size of the input, which reduces the number of parameters and computation in the network, and helps to control overfitting. Additionally, pooling layers can also help to make the network more invariant to small translations of the input. There are different types of pooling layers, such as max pooling, average pooling, and global pooling.
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