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Analysis of Gradient Degradation and Feature Map Quality in Deep All-Convolutional Neural Networks Compared to Deep Residual Networks

  • Wei GaoEmail author
  • Mark D. McDonnell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

The introduction of skip connections used for summing feature maps in deep residual networks (ResNets) were crucially important for overcoming gradient degradation in very deep convolutional neural networks (CNNs). Due to the strong results of ResNets, it is a natural choice to use features that it produces at various layers in transfer learning or for other feature extraction tasks. In order to analyse how the gradient degradation problem is solved by ResNets, we empirically investigate how discriminability changes as inputs propagate through the intermediate layers of two CNN variants: all-convolutional CNNs and ResNets. We found that the feature maps produced by residual-sum layers exhibit increasing discriminability with layer-distance from the input, but that feature maps produced by convolutional layers do not. We also studied how discriminability varies with training duration and the placement of convolutional layers. Our method suggests a way to determine whether adding extra layers will improve performance and show how gradient degradation impacts on which layers contribute increased discriminability.

Keywords

Convolutional Neural Networks Deep residual networks Deep features Image classification Phoneme classification CIFAR TIMIT 

Notes

Acknowledgements

This work is funded by Australian Government Research Training Program (RTP) Scholarship.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computational Learning Systems Laboratory, School of Information Technology and Mathematical SciencesUniversity of South AustraliaMawson LakesAustralia

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