Methods for Pruning Deep Neural Networks

Understand

This paper presents a survey of methods for pruning deep neural networks.

• It begins by categorising over 150 studies based on the underlying approach used and then focuses on three categories: methods that use magnitude based pruning, methods that utilise clustering to identify redundancy, and methods that use sensitivity analysis to assess the effect of pruning.
• Some of the key influencing studies within these categories are presented to highlight the underlying approaches and results achieved.
• Most studies present results which are distributed in the literature as new architectures, algorithms and data sets have developed with time, making comparison across different studied difficult.