Software for dataset-wide XAI: From local explanations to global insights with Zennit, CoRelAy, and ViRelAy.

ABSTRACT: The predictive capabilities of Deep Neural Networks (DNNs) are well-established, yet the underlying mechanisms driving these predictions often remain opaque. The advent of Explainable Artificial Intelligence (XAI) has introduced novel methodologies to explore the reasoning behind complex model predictions of complex models. Among post-hoc attribution methods, Layer-wise Relevance Propagation (LRP) has demonstrated notable adaptability and performance for explaining individual predictions - provided the method is used to its full potential. For deeper dataset-wide and quantitative analyses, however, the manual inspection of individual attribution maps remains unnecessarily labor-intensive and time consuming. While several approaches for dataset-wide XAI-analyses have been proposed, unified

SUBMITTER: Anders CJ 

PROVIDER: S-EPMC12758825 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature