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A Survey of Explainable AI in Deep Visual Modeling: Methods and Metrics

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arxiv 2301.13445 v1 pith:BGXR3277 submitted 2023-01-31 cs.CV cs.AIcs.LG

Naveed Akhtar This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords deepmetricsvisualalongexplainablemethodsmodelsresearch
verification ladder T0 review T1 audit T2 compute T3 formal
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Deep visual models have widespread applications in high-stake domains. Hence, their black-box nature is currently attracting a large interest of the research community. We present the first survey in Explainable AI that focuses on the methods and metrics for interpreting deep visual models. Covering the landmark contributions along the state-of-the-art, we not only provide a taxonomic organization of the existing techniques, but also excavate a range of evaluation metrics and collate them as measures of different properties of model explanations. Along the insightful discussion on the current trends, we also discuss the challenges and future avenues for this research direction.

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Cited by 2 Pith papers

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