Convolutional Neural Networks



Convolutional neural networks are designed to work with grid-structured inputs, which have strong spatial dependencies in local regions of the grid. The most obvious example of grid-structured data is a 2-dimensional image. This type of data also exhibits spatial dependencies, because adjacent spatial locations in an image often have similar color values of the individual pixels. An additional dimension captures the different colors, which creates a 3-dimensional input volume. Therefore, the features in a convolutional neural network have dependencies among one another based on spatial distances.


Convolution Neural Network AlexNet Convolutional Autoencoder Footprint Parameters Skip Connections 
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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IBM T. J. Watson Research CenterInternational Business MachinesYorktown HeightsUSA

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