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Deep Neural Networks (DNNs) are known to be strong predictors, but theirprediction strategies can rarely be understood. With recent advances inExplainable Artificial Intelligence (XAI), approaches are available to explorethe reasoning behind those complex models' predictions. Among post-hocattribution methods, Layer-wise Relevance Propagation (LRP) shows highperformance. For deeper quantitative analysis, manual approaches exist, butwithout the right tools they are unnecessarily labor intensive. In thissoftware paper, we introduce three software packages targeted at scientists toexplore model reasoning using attribution approaches and beyond: (1) Zennit - ahighly customizable and intuitive attribution framework implementing LRP andrelated approaches in PyTorch, (2) CoRelAy - a framework to easily and quicklyconstruct quantitative analysis pipelines for dataset-wide analyses ofexplanations, and (3) ViRelAy - a web-application to interactively exploredata, attributions, and analysis results. With this, we provide a standardizedimplementation solution for XAI, to contribute towards more reproducibility inour field.

Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy