  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.
