论文标题
配克摄像头：卷积神经网络的快速无梯度视觉解释
Recipro-CAM: Fast gradient-free visual explanations for convolutional neural networks
论文作者
论文摘要
卷积神经网络（CNN）是用于计算机视觉的广泛使用的深度学习体系结构。但是，其黑匣子性质使得很难解释模型的行为。为了减轻此问题，AI从业人员探索了可解释的AI方法，例如类激活图（CAM）和GRAD-CAM。尽管这些方法已显示出希望，但它们受到建筑约束或梯度计算负担的限制。为了克服这个问题，已经提出了作为无梯度方法的得分摄像机和消融摄像机，但是与基于CAM或GRAD-CAM的方法相比，它们具有更长的执行时间，使它们不适合现实世界解决方案，尽管它们解决了梯度相关问题并启用了推论模式XAI。为了应对这一挑战，我们提出了一种快速无梯度的相互凸轮（配克摄像头）方法。我们的方法涉及在空间掩盖提取的特征图以利用目标类别的激活图和网络预测之间的相关性。我们提出的方法产生了令人鼓舞的结果，在平均跌落相关 - 复杂性（ADCC）度量中的最新方法优于$ 1.78 \％$至$ 3.72 \％$ $，不包括VGG-16骨链。此外，Complo-CAM以与Grad-CAM相似的速度生成显着图，并且比Score-Cam的$ 148 $ 148倍。我们的数据分析框架可用来提供配克摄像机的源代码。
The Convolutional Neural Network (CNN) is a widely used deep learning architecture for computer vision. However, its black box nature makes it difficult to interpret the behavior of the model. To mitigate this issue, AI practitioners have explored explainable AI methods like Class Activation Map (CAM) and Grad-CAM. Although these methods have shown promise, they are limited by architectural constraints or the burden of gradient computing. To overcome this issue, Score-CAM and Ablation-CAM have been proposed as gradient-free methods, but they have longer execution times compared to CAM or Grad-CAM based methods, making them unsuitable for real-world solution though they resolved gradient related issues and enabled inference mode XAI. To address this challenge, we propose a fast gradient-free Reciprocal CAM (Recipro-CAM) method. Our approach involves spatially masking the extracted feature maps to exploit the correlation between activation maps and network predictions for target classes. Our proposed method has yielded promising results, outperforming current state-of-the-art method in the Average Drop-Coherence-Complexity (ADCC) metric by $1.78 \%$ to $3.72 \%$, excluding VGG-16 backbone. Moreover, Recipro-CAM generates saliency maps at a similar rate to Grad-CAM and is approximately $148$ times faster than Score-CAM. The source code for Recipro-CAM is available in our data analysis framework.
