As complex artificial intelligence (AI) systems such as deep neural networks is used for many mission critical task such as military, finance, human resources and autonomous driving, it is important to secure the safe use of such complex AI systems. In this talk, we will present recent advances to clarify the internal decision of deep neural networks. Moreover, we will overview approaches to automatically correct internal nodes which incur artifacts or less reliable outputs. Furthermore, we will investigate the reasons why some deep neural networks include not-so-stable internal nodes.
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