Integrative CAM: Adaptive Layer Fusion for Comprehensive Interpretation of CNNs

Integrative CAM is an advanced technique for deep learning models, offering a comprehensive view of feature importance across CNNs. It addresses limitations of traditional gradient-based methods by fusing insights from all network layers, using both gradient and activation scores to adaptively weight layer contributions. This approach includes a novel bias term in saliency map calculation, essential for capturing feature importance. Integrative CAM generalizes the alpha term from Grad-CAM++ for wider model applicability. Through extensive experiments, it demonstrates superior fidelity in feature importance mapping, enhancing interpretability for complex decision-making tasks. This method provides a valuable tool for fusion-driven applications, promoting the deployment of deep learning models with trustworthiness and insight.