What is the role of max pooling operation in neural network

This recipe explains what is the role of max pooling operation in neural network

Recipe Objective - What is the role of max-pooling operation in neural network?

Max-pooling operation in neural network is to aggressively downsample the feature maps much like the strided convolutions. Max pooling operation consists of extracting the windows from input feature maps and outputting the max value of each channel. It’s conceptually similar to convolution except that instead of transforming local patches through a learned linear transformation (a convolution kernel), they are transformed through a hard-coded tensor operation. A big max difference from convolution is that max-pooling operation is usually done with 2 × 2 windows and stride 2 that is in order to downsample the feature maps by a factor of 2. On the other hand, Convolution is done with 3 × 3 windows and no stride that is stride 1.

A Deep Dive into the Types of Neural Networks

Explanation of max-pooling operation.

Max-pooling is used to reduce the number of feature-map coefficients to process as well as to induce the spatial-filter hierarchies by making the successive convolution layers look at increasingly large windows that is in terms of the fraction of the original input it covers.

Alternatively strides in convolutional layer and average pooling can be used for downsampling but max-pooling operation work better than these solutions as in max-pooling operation, features tend to encode spatial presence of some pattern or concept over the different tiles of feature map and it is more informative to look at maximal presence of different features than at its average presence.

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