# Taxonomy of Saliency Metrics for Channel Pruning

arXiv: https://arxiv.org/abs/1906.04675 · HTML: https://mlsanity.com/paper/1906.04675/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Information](https://mlsanity.com/paper/2205.02131/) (2022)
- [DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack](https://mlsanity.com/paper/2311.08909/) (2023)

## Abstract (arXiv)

Pruning unimportant parameters can allow deep neural networks (DNNs) to reduce their heavy computation and memory requirements. A saliency metric estimates which parameters can be safely pruned with little impact on the classification performance of the DNN. Many saliency metrics have been proposed, each within the context of a wider pruning algorithm. The result is that it is difficult to separate the effectiveness of the saliency metric from the wider pruning algorithm that surrounds it. Similar-looking saliency metrics can yield very different results because of apparently minor design choices. We propose a taxonomy of saliency metrics based on four mostly-orthogonal principal components. We show that a broad range of metrics from the pruning literature can be grouped according to these components. Our taxonomy not only serves as a guide to prior work, but allows us to construct new saliency metrics by exploring novel combinations of our taxonomic components. We perform an in-depth experimental investigation of more than 300 saliency metrics. Our results provide decisive answers to open research questions, and demonstrate the importance of reduction and scaling when pruning groups of weights. We find that some of our constructed metrics can outperform the best existing state-of-the-art metrics for convolutional neural network channel pruning.