May 27, 2024 · In this article, we propose a novel masking method to enhance the reliability and efficiency of the SHAP method in SAR-ATR applications.
May 30, 2024 · One approach to tackle this challenge is the SHAP method. It enhances the explainability of deep neural networks in SAR target recognition by ...
NASA/ADS · Deep Neural Network Explainability Enhancement via Causality-Erasing SHAP Method for SAR Target Recognition.
A novel masking method is proposed to enhance the reliability and efficiency of the SHAP method in SAR-ATR applications and significantly enhances the ...
One approach to tackle this challenge is the SHAP method. It enhances the explainability of deep neural networks in SAR target recognition by observing how the ...
Jul 11, 2024 · A CAUSALITY-ERASING BASELINE VALUES OF SHAP METHOD FOR ENHANCING EXPLAINABLE DEEP NETWORKS IN SAR TARGET CLASSIFICATION · 1: AN ENHANCEMENT ...
Missing: Recognition. | Show results with:Recognition.
Explainability of Deep Networks for SAR Target Recognition via Class ...
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Jan 18, 2024 · This study introduces the concept of perturbation into CAM, proposing an algorithm called SAR Clutter Characteristics CAM (SCC-CAM).
Missing: Causality- Erasing
In this article, a set of new analytical tools is proposed and applied to a convolutional neural network (CNN) handling automatic target recognition on two SAR ...
Deep Neural Network Explainability Enhancement via Causality-Erasing SHAP Method for SAR Target Recognition · Z. CuiZhiyuan Yang +4 authors. Jianyu Yang.
Deep Neural Network Explainability Enhancement via Causality-Erasing SHAP Method for SAR Target Recognition. Request PDF. Restricted access. IEEE Transactions ...