Deep learning models have achieved remarkable success across diverse domains. However, the intricate nature of these models often impedes a clear understanding of their decision-making processes. This is where Explainable AI (XAI) becomes indispensable, offering intuitive explanations for model decisions. In this work, we propose a simple yet highly effective approach, ScoreCAM++, which introduces modifications to enhance the promising ScoreCAM method for visual explainability. Our proposed approach involves altering the normalization function within the activation layer utilized in ScoreCAM, resulting in significantly improved results compared to previous efforts. Additionally, we apply an activation function to the upsampled activation layers to enhance interpretability. This improvement is achieved by selectively gating lower-priority values within the activation layer. Through extensive experiments and qualitative comparisons, we demonstrate that ScoreCAM++ consistently achieves notably superior performance and fairness in interpreting the decision-making process compared to both ScoreCAM and previous methods.

翻译：深度学习模型已在各领域取得显著成功。然而，这些模型的复杂本质常阻碍对其决策过程的清晰理解。这正是可解释人工智能（XAI）不可或缺之处——它为模型决策提供直观解释。本文提出一种简洁高效的方案ScoreCAM++，通过引入改进措施增强具有前景的视觉可解释方法ScoreCAM。我们的方法通过修改ScoreCAM中激活层的归一化函数，相较于先前工作取得显著更优结果。此外，我们对上采样激活层应用激活函数以增强可解释性，该改进通过选择性门控激活层中优先级较低的值实现。通过大量实验与定性比较，我们证明ScoreCAM++在解释决策过程时，始终在性能和公平性上优于ScoreCAM及先前方法。