论文标题
SS-CAM：平滑的得分摄像机，用于更清晰的视觉特征本地化
SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization
论文作者
论文摘要
由于它们在高风险环境中的应用，对深卷卷神经网络的潜在机制的解释已成为深度学习领域的重要方面。为了解释这些黑盒架构，已经采用了许多方法，因此可以分析和理解内部决策。在本文中，基于评分摄像机的顶部，我们从称为SS-CAM的视觉清晰度引入了增强的视觉解释，该解释通过平滑的操作在图像中产生对象特征的集中定位。我们在ILSVRC 2012验证数据集上评估了我们的方法，该数据集在忠诚和本地化任务上都优于得分板。
Interpretation of the underlying mechanisms of Deep Convolutional Neural Networks has become an important aspect of research in the field of deep learning due to their applications in high-risk environments. To explain these black-box architectures there have been many methods applied so the internal decisions can be analyzed and understood. In this paper, built on the top of Score-CAM, we introduce an enhanced visual explanation in terms of visual sharpness called SS-CAM, which produces centralized localization of object features within an image through a smooth operation. We evaluate our method on the ILSVRC 2012 Validation dataset, which outperforms Score-CAM on both faithfulness and localization tasks.
