FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks

TL;DR

FastCAV significantly accelerates the computation of Concept Activation Vectors, enabling scalable and efficient concept-based explanations of deep neural networks without sacrificing accuracy.

Contribution

We introduce FastCAV, a method that reduces CAV computation time by up to 63.6x, with a theoretical foundation and empirical validation showing maintained performance.

Findings

FastCAV achieves up to 63.6x speedup in CAV computation.

CAVs computed with FastCAV are as accurate and stable as traditional methods.

FastCAV enables new analyses like tracking concept evolution during training.

Abstract

Concepts such as objects, patterns, and shapes are how humans understand the world. Building on this intuition, concept-based explainability methods aim to study representations learned by deep neural networks in relation to human-understandable concepts. Here, Concept Activation Vectors (CAVs) are an important tool and can identify whether a model learned a concept or not. However, the computational cost and time requirements of existing CAV computation pose a significant challenge, particularly in large-scale, high-dimensional architectures. To address this limitation, we introduce FastCAV, a novel approach that accelerates the extraction of CAVs by up to 63.6x (on average 46.4x). We provide a theoretical foundation for our approach and give concrete assumptions under which it is equivalent to established SVM-based methods. Our empirical results demonstrate that CAVs calculated with FastCAV maintain similar performance while being more efficient and stable. In downstream applications, i.e., concept-based explanation methods, we show that FastCAV can act as a replacement leading to equivalent insights. Hence, our approach enables previously infeasible investigations of deep models, which we demonstrate by tracking the evolution of concepts during model training.