论文标题
评估LEGO多标签图像分类任务上的石灰和Grad-CAM解释方法的性能
Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task
论文作者
论文摘要
在本文中，我们在一个卷积神经网络上运行了两种解释方法，即石灰和毕业-CAM，该卷积神经网络训练了用它们可见的乐高积木标记图像的图像。我们根据两个标准对它们进行评估，即网络的核心性能以及他们能够为系统用户产生的信任。我们发现，总体而言，Grad-CAM似乎在这项特定任务上表现出色：从核心绩效的角度来看，它会产生更详细的见解，而80％的受访者在涉及他们在模型中启发的信任时，要求他们在模型中选择Grad-CAM。但是，我们还认为将这两种方法一起使用更有用，因为它们产生的见解是互补的。
In this paper, we run two methods of explanation, namely LIME and Grad-CAM, on a convolutional neural network trained to label images with the LEGO bricks that are visible in them. We evaluate them on two criteria, the improvement of the network's core performance and the trust they are able to generate for users of the system. We find that in general, Grad-CAM seems to outperform LIME on this specific task: it yields more detailed insight from the point of view of core performance and 80\% of respondents asked to choose between them when it comes to the trust they inspire in the model choose Grad-CAM. However, we also posit that it is more useful to employ these two methods together, as the insights they yield are complementary.
