Investigating the Effect of Network Pruning on Performance and Interpretability

TL;DR

This paper examines how different network pruning techniques affect the performance and interpretability of GoogLeNet, revealing that high interpretability scores can occur even with low accuracy and that retraining can restore performance.

Contribution

It systematically compares various pruning methods and retraining strategies, and evaluates their effects on both accuracy and interpretability using the MIS metric.

Findings

Sufficient retraining can restore or improve performance after pruning.

No significant correlation between pruning rate and interpretability as measured by MIS.

Low-accuracy networks can still have high interpretability scores.

Abstract

Deep Neural Networks (DNNs) are often over-parameterized for their tasks and can be compressed quite drastically by removing weights, a process called pruning. We investigate the impact of different pruning techniques on the classification performance and interpretability of GoogLeNet. We systematically apply unstructured and structured pruning, as well as connection sparsity (pruning of input weights) methods to the network and analyze the outcomes regarding the network's performance on the validation set of ImageNet. We also compare different retraining strategies, such as iterative pruning and one-shot pruning. We find that with sufficient retraining epochs, the performance of the networks can approximate the performance of the default GoogLeNet - and even surpass it in some cases. To assess interpretability, we employ the Mechanistic Interpretability Score (MIS) developed by Zimmermann et al. . Our experiments reveal that there is no significant relationship between interpretability and pruning rate when using MIS as a measure. Additionally, we observe that networks with extremely low accuracy can still achieve high MIS scores, suggesting that the MIS may not always align with intuitive notions of interpretability, such as understanding the basis of correct decisions.