DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks.

Abstract

The ability to explain the outputs of deep neural networks is crucial, particularly in safety-critical applications. Recently, complex-valued neural networks (CVNNs) have gained increasing popularity; however no dedicated framework for explaining their predictions has been introduced. To address this gap, we develop DeepCSHAP, a method that extends the widely used SHAP framework to CVNNs. In addition, we adapt common gradient-based explanation techniques to the complex domain using Wirtinger derivatives and provide both these methods and DeepCSHAP in an open-source explainability library. Experimental results demonstrate that DeepCSHAP outperforms other explanation methods in explaining model outputs of CVNNs.