Enhancing Explainability in Convolutional Neural Networks Using Entropy-Based Class Activation Maps

With the emergence of visual sensors and their widespread application in intelligent systems, precise and interpretable visual explanations have become essential for ensuring the reliability and effectiveness of these systems. Sensor data, such as that from cameras operating in different spectra, LiDAR, or other imaging modalities, is often processed using complex deep learning methods, whose decision-making processes can be unclear. Accurate interpretation of network decisions is particularly critical in domains such as autonomous vehicles, medical imaging, and security systems. Moreover, during the development and deployment of deep learning architectures, the ability to accurately interpret results is crucial for identifying and mitigating any sources of bias in the training data, thereby ensuring fairness and robustness in the model's performance. Explainable AI (XAI) techniques have garnered significant interest for their ability to reveal the rationale behind network decisions. In this work, we propose leveraging entropy information to enhance Class Activation Maps (CAMs). We explore two novel approaches: the first replaces the traditional gradient averaging scheme with entropy values to generate feature map weights, while the second directly utilizes entropy to weigh and sum feature maps, thereby reducing reliance on gradient-based methods, which can sometimes be unreliable. Our results demonstrate that entropy-based CAMs offer significant improvements in highlighting relevant regions of the input across various scenarios.