Part-based Quantitative Analysis for Heatmaps

• URL: http://arxiv.org/abs/2405.13264v1
• Date: Wed, 22 May 2024 00:24:17 GMT
• Title: Part-based Quantitative Analysis for Heatmaps
• Authors: Osman Tursun, Sinan Kalkan, Simon Denman, Sridha Sridharan, Clinton Fookes,
• Abstract summary: Heatmaps have been instrumental in helping understand deep network decisions, and are a common approach for Explainable AI (XAI) Heatmap analysis is typically very subjective and limited to domain experts.
• Score: 49.473051402754486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heatmaps have been instrumental in helping understand deep network decisions, and are a common approach for Explainable AI (XAI). While significant progress has been made in enhancing the informativeness and accessibility of heatmaps, heatmap analysis is typically very subjective and limited to domain experts. As such, developing automatic, scalable, and numerical analysis methods to make heatmap-based XAI more objective, end-user friendly, and cost-effective is vital. In addition, there is a need for comprehensive evaluation metrics to assess heatmap quality at a granular level.

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