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Investigating the Not-So-Obvious Effects of Structured Pruning

H. Tessier, V. Gripon, M. Léonardon, M. Arzel, D. Bertrand and T. Hannagan, "Investigating the Not-So-Obvious Effects of Structured Pruning," in ICML HAET Workshop, July 2022.

Structured pruning is a popular method to reduce the cost of convolutional neural networks. However, depending on the architecture, pruning introduces dimensional discrepancies which prevent the actual reduction of pruned networks and mask their true complexity. Most papers in the literature overlook these issues. We propose a method that systematically solves them and generate an operational network. We show through experiments the gap between the theoretical pruning ratio and the actual complexity revealed by our method.

Download manuscript.

  author = {Hugo Tessier and Vincent Gripon and
Mathieu Léonardon and Matthieu Arzel and David
Bertrand and Thomas Hannagan},
  title = {Investigating the Not-So-Obvious Effects of
Structured Pruning},
  booktitle = {ICML HAET Workshop},
  year = {2022},
  month = {July},

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