Pooling layers are placed between convolution layers. Pooling layers reduce the size of the image across layers by sampling. The sampling is done by selecting the maximum value in a window. Average pooling averages over the window. Pooling also acts as a regularization technique to avoid overfitting. Pooling is carried out on all the channels of features. Pooling can also be performed with various strides.

The size of the window is a measure of the receptive field of CNN. The following figure shows an example of max pooling:

CNN is the single most important component of any deep learning model for computer vision. It won’t be an exaggeration to state that it will be impossible for any computer to have vision without a CNN. In the next sections, we will discuss a couple of advanced layers that can be used for a few applications.

Visit https://www.youtube.com/watch?v=jajksuQW4mc for a great visualization of a CNN and max-pooling operation.