6 Wide convolutional neural networks

This chapter covers

  • Introducing the wide convolutional layer design pattern
  • Understanding the advantages of wide versus deep layers
  • Refactoring micro-architecture patterns to decrease computational complexity
  • Coding former SOTA wide convolutional models with the procedural design pattern

Up to now, we’ve focused on networks with deeper layers, block layers, and shortcuts in residual networks for image-related tasks such as classification, object localization, and image segmentation. Now we are going to take a look at networks with wide, rather than deep, convolutional layers. Starting in 2014 with Inception v1 (GoogLeNet) and 2015 with ResNeXt and Inception v2, neural network designs moved into wide layers, ...

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