论文标题
通过Riemann-Stieltjes集成基于梯度的本地化，深网的视觉解释
Visual Explanations from Deep Networks via Riemann-Stieltjes Integrated Gradient-based Localization
论文作者
论文摘要
神经网络在涉及分类和识别图像的任务上变得越来越好。同时，已经提出了旨在解释网络输出的技术。一种这样的技术是基于梯度的类激活图（GRAD-CAM），它能够在卷积神经网络（CNN）的各个级别上找到输入图像的特征，但对消失的梯度问题敏感。有一些不受该问题影响的技术，例如集成梯度（IG），但其使用仅限于网络的输入层。在这里，我们介绍了一种新技术，以产生视觉解释，以预测CNN的预测。像Grad-CAM一样，我们的方法可以应用于网络的任何层，并且像集成的梯度一样，它不受消失梯度的问题的影响。为了效率，使用riemann-stieltjes总和在数值上进行梯度积分。与Grad-CAM相比，我们的算法产生的热图更好地集中在感兴趣的领域，并且它们的数值计算更加稳定。我们的代码可从https://github.com/mlerma54/rsigradcam获得
Neural networks are becoming increasingly better at tasks that involve classifying and recognizing images. At the same time techniques intended to explain the network output have been proposed. One such technique is the Gradient-based Class Activation Map (Grad-CAM), which is able to locate features of an input image at various levels of a convolutional neural network (CNN), but is sensitive to the vanishing gradients problem. There are techniques such as Integrated Gradients (IG), that are not affected by that problem, but its use is limited to the input layer of a network. Here we introduce a new technique to produce visual explanations for the predictions of a CNN. Like Grad-CAM, our method can be applied to any layer of the network, and like Integrated Gradients it is not affected by the problem of vanishing gradients. For efficiency, gradient integration is performed numerically at the layer level using a Riemann-Stieltjes sum approximation. Compared to Grad-CAM, heatmaps produced by our algorithm are better focused in the areas of interest, and their numerical computation is more stable. Our code is available at https://github.com/mlerma54/RSIGradCAM
