Forward and Backward propagation of Max Pooling Layer in Convolutional Neural Networks

Theory and Code

Introduction

In the last article we saw how to do forward and backward propagation for convolution operations in CNNs. It was found that applying the pooling layer after the convolution layer improves performance helping the network to generalize better and reduce overfitting. This is because, given a certain grid (pooling height x pooling width) we sample only one value from it ignoring particular elements and suppressing noise. Moreover, because pooling reduces the spatial dimension of the feature …

backpropagation convolutional-network convolutional neural networks deep learning image processing networks neural networks pooling