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 ...
