A Gentle Introduction to Pooling Layers for Convolutional Neural Networks

Last Updated on July 5, 2019

Convolutional layers in a convolutional neural network summarize the presence of features in an input image.

A problem with the output feature maps is that they are sensitive to the location of the features in the input. One approach to address this sensitivity is to down sample the feature maps. This has the effect of making the resulting down sampled feature maps more robust to changes in the position of the feature in the image, referred to by the technical phrase “local translation invariance.”

Pooling layers provide an approach to down sampling feature maps by summarizing the presence of features in patches of the feature map. Two common pooling methods are average pooling and max pooling that summarize the average presence of a feature and the most activated presence of a feature respectively.

In this tutorial, you will discover how the pooling operation works and how to implement it in convolutional neural networks.

After completing this tutorial, you will know:

• Pooling is required to down sample the detection of features in feature maps.
• How to calculate and implement average and maximum pooling in a convolutional neural network.
• How to use global
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