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

arXiv:2403.08428v1 Announce Type: new
Abstract: 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 …

abstract applications arxiv autonomous autonomous driving corporate cs.lg driving health health care networks neural networks public safety type values