Robust Explainability: A tutorial on gradient-based attribution methods for deep neural networks
Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Ravi P. Ramachandran, Nidhal Carla Bouaynaya · IEEE Signal Processing Magazine · 2022
The rise in deep neural networks (DNNs) has led to increased interest in explaining their predictions. While many methods for this exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning (DL) research; however, it has been hardly talked about in explainability until very recently.