DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks
This work addresses the need for explanation algorithms in complex-valued neural networks, which is important for safety-critical applications like healthcare and autonomous driving, but it is incremental as it adapts existing real-valued methods.
The authors tackled the problem of explaining complex-valued neural networks by adapting DeepSHAP and four gradient-based methods to the complex domain, resulting in an open-source library that is adaptable to recent architectures.
Deep Neural Networks are widely used in academy as well as corporate and public applications, including safety critical applications such as health care and autonomous driving. The ability to explain their output is critical for safety reasons as well as acceptance among applicants. A multitude of methods have been proposed to explain real-valued neural networks. Recently, complex-valued neural networks have emerged as a new class of neural networks dealing with complex-valued input data without the necessity of projecting them onto $\mathbb{R}^2$. This brings up the need to develop explanation algorithms for this kind of neural networks. In this paper we provide these developments. While we focus on adapting the widely used DeepSHAP algorithm to the complex domain, we also present versions of four gradient based explanation methods suitable for use in complex-valued neural networks. We evaluate the explanation quality of all presented algorithms and provide all of them as an open source library adaptable to most recent complex-valued neural network architectures.