A Gentle Introduction to 1×1 Convolutions to Manage Model Complexity

Last Updated on July 5, 2019

Pooling can be used to down sample the content of feature maps, reducing their width and height whilst maintaining their salient features.

A problem with deep convolutional neural networks is that the number of feature maps often increases with the depth of the network. This problem can result in a dramatic increase in the number of parameters and computation required when larger filter sizes are used, such as 5×5 and 7×7.

To address this problem, a 1×1 convolutional layer can be used that offers a channel-wise pooling, often called feature map pooling or a projection layer. This simple technique can be used for dimensionality reduction, decreasing the number of feature maps whilst retaining their salient features. It can also be used directly to create a one-to-one projection of the feature maps to pool features across channels or to increase the number of feature maps, such as after traditional pooling layers.

In this tutorial, you will discover how to use 1×1 filters to control the number of feature maps in a convolutional neural network.

After completing this tutorial, you will know:

• The 1×1 filter can be used to create a linear projection
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