Pooling

Pooling is a technique used in deep learning to reduce the spatial dimensions of feature maps. It involves dividing the input image into non-overlapping regions and then computing the maximum, minimum, or average value of each region. This process helps to reduce the number of parameters in the model and prevent overfitting. Pooling can be performed on different levels of the network, and the size of the pooling window can be adjusted to control the degree of downsampling. However, pooling can also result in loss of information, especially in cases where the input image is already small or the pooling window is too large.

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Pooling

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