Summary of Layer Pruning with Consensus: a Triple-win Solution, by Leandro Giusti Mugnaini et al.
Layer Pruning with Consensus: A Triple-Win Solution
by Leandro Giusti Mugnaini, Carolina Tavares Duarte, Anna H. Reali Costa, Artur Jordao
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to layer pruning that combines multiple similarity metrics into a single measure called the Consensus criterion. The technique aims to detect unimportant layers for removal while minimizing accuracy drop and maximizing performance improvement. The proposed method achieves a triple-win solution, reducing computational costs, latency, and memory footprint by up to 78.80%, with improved robustness against adversarial attacks and energy consumption reduced by up to 66.99%. The Consensus criterion demonstrates its effectiveness in creating robust, efficient, and environmentally friendly pruned models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Layer pruning is a technique that reduces the computational costs of neural networks by removing unimportant layers. This paper proposes a new way to do this using multiple similarity metrics combined into one measure called the Consensus criterion. The goal is to find layers that are not important for the network’s performance and remove them, without hurting the overall accuracy too much. The method does this well, reducing energy consumption by up to 66.99% and improving robustness against attacks. |
Keywords
» Artificial intelligence » Pruning
