Why do we use a Pooling Layer in a CNN?

Data Science, AI and ML
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#1

CNN uses pooling layers to reduce the size of the input image so that it speeds up the computation of the network.

Pooling or spatial pooling layers: Also called subsampling or downsampling.

  • It is applied after convolution and RELU operations.
  • It reduces the dimensionality of each feature map by retaining the most important information.
  • Since the number of hidden layers required to learn the complex relations present in the image would be large.

As a result of pooling, even if the picture were a little tilted, the largest number in a certain region of the feature map would have been recorded and hence, the feature would have been preserved. Also as another benefit, reducing the size by a very significant amount will use less computational power. So, it is also useful for extracting dominant features.