Explainable artificial intelligence (XAI): How to make image analysis deep learning models transparent
Haekang Song, Sungho Kim · 2022 22nd International Conference on Control, Automation and Systems (ICCAS) · 2022
Recently, Deep learning (DL) model has made remarkable achievements in image processing. To increase the accuracy of the DL model, more parameters are used. Therefore, the current DL models are black-box models that cannot understand the internal structure. This is the reason why the DL model cannot be applied to fields where stability and reliability are important despite its high performance. In this paper, We investigated various Explainable artificial intelligence (XAI) techniques to solve this problem. We also investigated what approaches exist to make multi-modal deep learning models transparent.