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

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arxiv 2106.13200 v2 pith:IUWWGIGS submitted 2021-06-24 cs.LG

Christopher J. Anders , David Neumann , Wojciech Samek , Klaus-Robert Müller , Sebastian Lapuschkin This is my paper
classification cs.LG
keywords approachesanalysisattributionexploresoftwarecorelaydataset-wideexplanations
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Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood. With recent advances in Explainable Artificial Intelligence (XAI), approaches are available to explore the reasoning behind those complex models' predictions. Among post-hoc attribution methods, Layer-wise Relevance Propagation (LRP) shows high performance. For deeper quantitative analysis, manual approaches exist, but without the right tools they are unnecessarily labor intensive. In this software paper, we introduce three software packages targeted at scientists to explore model reasoning using attribution approaches and beyond: (1) Zennit - a highly customizable and intuitive attribution framework implementing LRP and related approaches in PyTorch, (2) CoRelAy - a framework to easily and quickly construct quantitative analysis pipelines for dataset-wide analyses of explanations, and (3) ViRelAy - a web-application to interactively explore data, attributions, and analysis results. With this, we provide a standardized implementation solution for XAI, to contribute towards more reproducibility in our field.

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Cited by 2 Pith papers

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  1. From Clever Hans to Scientific Discovery: Interpreting EEG Foundational Transformers with LRP

    cs.AI 2026-05 unverdicted novelty 6.0 of 10

    LRP on EEG transformers reveals Clever Hans artifacts in motor imagery tasks and a recurring central electrode cluster as a candidate sensorimotor signature of arousal.

  2. Does Aurora Encode Atmospheric Structure? Latent Regime Analysis and Attribution

    cs.LG 2026-06 unverdicted novelty 4.0 of 10

    Aurora's latent space is organized by seasonal cycles with evidence of encoding 3D vertical atmospheric structure for storms, confirmed by perturbation experiments.

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