Fusion Grad-CAM: A Methodology for Generating Unified Attention Maps in Majority Voting Classifiers

In this work, we introduce a novel methodology called Fusion Grad-CAM for generating attention maps in ensemble classification systems that utilize majority voting. Currently, there is no established technique for producing a consolidated attention map in such systems. Traditional approaches require inspecting individual attention maps from each contributing model, which is inefficient. Our proposed method integrates Grad-CAM produced attention maps by averaging or weighted averaging them based on both the predicted class and the confidence level of each model’s decision. This approach offers a holistic representation of the ensemble’s decision-making process. We validate our methodology using various combinations of well-known convolutional neural networks (CNNs) pretrained on ImageNet, demonstrating the effectiveness and clarity of the resulting attention maps.