Abstract
Deep neural networks have achieved significant success across various fields, but their intrinsic black-box nature hinders the further development. Addressing the interpretability challenges, topological data analysis has emerged as a promising tool to reveal these complex models. In this work, we present a review of the emerging field of interpreting deep neural networks using topological data analysis. We organize the existing body of work into distinct analytical categories, highlighting interpretations based on the topology of data, network structural characteristics, network functional characteristics, and techniques derived from Mapper. The objective of this paper is to extract the research pattern of this area, and point out the future research direction.
Published Version
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