Measuring Per-Unit Interpretability at Scale Without Humans

Roland S. Zimmermann, David Klindt, Wieland Brendel

Keywords: interpretability, explainability, deep learning, neural networks, analysis, activation maximization, alignment, evaluation

TL;DR: We introduce the first scalable method to measure the per-unit interpretability in vision neural networks and demonstrate its alignment to human judgements.

Abstract: In today’s era, whatever we can measure at scale, we can optimize. So far, measuring the interpretability of units in deep neural networks (DNNs) for computer vision still requires direct human evaluation and is not scalable. As a result, the inner workings of DNNs remain a mystery despite the remarkable progress we have seen in their applications. In this work, we introduce the first scalable method to measure the per-unit interpretability in vision DNNs. This method does not require any human evaluations, yet its prediction correlates well with existing human interpretability measurements. We validate its predictive power through an interventional human psychophysics study. We demonstrate the usefulness of this measure by performing previously infeasible experiments: (1) A large-scale interpretability analysis across more than 70 million units from 835 computer vision models, and (2) an extensive analysis of how units transform during training. We find an anticorrelation between a model's downstream classification performance and per-unit interpretability, which is also observable during model training. Furthermore, we see that a layer's location and width influence its interpretability.

Primary Area: Interpretability and explainability

Submission Number: 6325