Abstract
Gradient-based saliency maps have been widely used to explain the decisions of deep neural network classifiers. However, standard gradient-based interpretation maps, including the simple gradient and integrated gradient algorithms, often lack desired structures such as sparsity and connectedness in their application to real-world computer vision models. A frequently used approach to inducing sparsity structures into gradient-based saliency maps is to alter the simple gradient scheme using sparsification or norm-based regularization. A drawback with such post-processing methods is their frequently-observed significant loss in fidelity to the original simple gradient map. In this work, we propose to apply adversarial training as an in-processing scheme to train neural networks with structured simple gradient maps. We show a duality relation between the regularized norms of the adversarial perturbations and gradient-based maps, based on which we design adversarial training loss functions promoting sparsity and group-sparsity properties in simple gradient maps. We present several numerical results to show the influence of our proposed norm-based adversarial training methods on the standard gradient-based maps of standard neural network architectures on benchmark image datasets.
Abstract (translated)
基于梯度的显着性图已被广泛用于解释深度神经网络分类器的决策。然而,标准的基于梯度的解释图,包括简单的梯度和集成梯度算法,通常缺乏其在现实世界计算机视觉模型上的所需的结构,如稀疏性和连通性。一种常用的将稀疏结构诱导到基于梯度的显着性图的方法是使用稀疏化或基于规范的 Regularization。然而,这种后处理方法经常观察到对原始简单梯度图的保真度显著下降。在本文中,我们将 adversarial 训练作为一种加工方案应用于具有结构化简单梯度图的神经网络的训练中。我们基于规范的梯度扰动的有界性和梯度-基于地图的稀疏性和群稀疏性性质,设计了一种促进简单梯度图稀疏性和群稀疏性特性的 adversarial 训练损失函数。我们提供了几个数值结果,以展示我们的基于规范的 adversarial 训练方法对标准神经网络架构标准梯度-基于地图的显着性图的影响。
URL
https://arxiv.org/abs/2404.04647
