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arxiv: 2010.03023 · v1 · pith:3R365WLN · submitted 2020-10-06 · cs.CV

IS-CAM: Integrated Score-CAM for axiomatic-based explanations

Rakshit Naidu , Ankita Ghosh , Yash Maurya , Shamanth R Nayak K , Soumya Snigdha Kundu This is my paper

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classification cs.CV
keywords is-camscore-camintegratedmodelsaccountachieveattemptattribution
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Convolutional Neural Networks have been known as black-box models as humans cannot interpret their inner functionalities. With an attempt to make CNNs more interpretable and trustworthy, we propose IS-CAM (Integrated Score-CAM), where we introduce the integration operation within the Score-CAM pipeline to achieve visually sharper attribution maps quantitatively. Our method is evaluated on 2000 randomly selected images from the ILSVRC 2012 Validation dataset, which proves the versatility of IS-CAM to account for different models and methods.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. How Can One Choose the Best CAM-Based Explainability Method for a CNN Model?

    cs.LG 2026-05 unverdicted novelty 5.0

    Manhattan and Correlation distance metrics best align CAM saliency maps with human perception on ImageNet chihuahuas, ranking LayerCAM, Score-CAM, and IS-CAM highest when compared to crowdsourced choices via RBO.