A model-agnostic approach for generating Saliency Maps to explain inferred decisions of Deep Learning Models. (arXiv:2209.08906v2 [cs.CV] UPDATED)

The widespread use of black-box AI models has raised the need for algorithms
and methods that explain the decisions made by these models. In recent years,
the AI research community is increasingly interested in models' explainability
since black-box models take over more and more complicated and challenging
tasks. Explainability becomes critical considering the dominance of deep
learning techniques for a wide range of applications, including but not limited
to computer vision. In the direction of understanding the inference process of …

arxiv decisions deep learning maps model-agnostic