标题： 灵敏度分析方法在研究神经网络模型中的应用 显示英文标题

标题： Application of Sensitivity Analysis Methods for Studying Neural Network Models

摘要： 本研究展示了多种方法在分析神经网络对输入数据扰动的敏感性以及解释其潜在机制方面的能力。 所研究的方法包括Sobol全局敏感性分析、输入像素扰动的局部敏感性方法以及激活最大化技术。 作为示例，本研究考虑了一个用于分析开放表格临床糖尿病数据集的小型前馈神经网络，以及两个经典的卷积架构VGG-16和ResNet-18，它们广泛用于图像处理和分类。 使用全局敏感性分析有助于识别所选小型神经网络的主要输入参数，并在不显著损失准确性的情况下减少其数量。 由于全局敏感性分析不适用于更大的模型，我们尝试在卷积神经网络中应用局部敏感性分析和激活最大化方法。 这些方法在解决图像分类问题的卷积模型中表现出有趣的模式。 总的来说，我们在超声数据分析的背景下将激活最大化方法的结果与流行的Grad-CAM技术进行了比较。 显示英文摘要

摘要： This study demonstrates the capabilities of several methods for analyzing the sensitivity of neural networks to perturbations of the input data and interpreting their underlying mechanisms. The investigated approaches include the Sobol global sensitivity analysis, the local sensitivity method for input pixel perturbations and the activation maximization technique. As examples, in this study we consider a small feedforward neural network for analyzing an open tabular dataset of clinical diabetes data, as well as two classical convolutional architectures, VGG-16 and ResNet-18, which are widely used in image processing and classification. Utilization of the global sensitivity analysis allows us to identify the leading input parameters of the chosen tiny neural network and reduce their number without significant loss of the accuracy. As far as global sensitivity analysis is not applicable to larger models we try the local sensitivity analysis and activation maximization method in application to the convolutional neural networks. These methods show interesting patterns for the convolutional models solving the image classification problem. All in all, we compare the results of the activation maximization method with popular Grad-CAM technique in the context of ultrasound data analysis.