<ul><li>CF-CAM is a novel framework that addresses the challenge of neural network decision-making opacity in deep learning.</li><li>Existing Class Activation Mapping (CAM) techniques for visualizing model decisions have trade-offs related to gradient perturbations and computational overhead.</li><li>CF-CAM employs a hierarchical importance weighting strategy and clustering techniques to enhance robustness against gradient noise and preserve discriminative features.</li><li>Experimental results show that CF-CAM outperforms state-of-the-art CAM methods in interpretability and robustness, making it suitable for critical applications like medical diagnosis and autonomous driving.</li></ul>

CF-CAM: Gradient Perturbation Mitigation and Feature Stabilization for Reliable Interpretability

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