最大化不变数据扰动的随机最优化
Maximizing Invariant Data Perturbation with Stochastic Optimization
摘要：
特征归因方法或显着图是解释复杂机器学习模型（如深度神经网络）决策的最流行方法之一。在这项研究中，我们提出了一个随机最优化方法的扰动为基础的特征属性的方法。而原来的优化问题的扰动为基础的特征属性是很难解决的，因为复杂的约束条件，我们建议重新制定的问题作为一个可微函数的最大化，这可以使用基于梯度的算法来解决。特别是，随机最优化是非常适合所提出的重新制定，我们可以解决这个问题，使用流行的算法，如SGD，RMSProp和亚当。使用VGG 16进行图像分类的实验表明，该方法可以有效地识别图像的相关部分。
英文摘要：
Feature attribution methods, or saliency maps, are one of the most popular approaches for explaining the decisions of complex machine learning models such as deep neural networks. In this study, we propose a stochastic optimization approach for the perturbation-based feature attribution method. While the original optimization problem of the perturbation-based feature attribution is difficult to solve because of the complex constraints, we propose to reformulate the problem as the maximization of a differentiable function, which can be solved using gradient-based algorithms. In particular, stochastic optimization is well-suited for the proposed reformulation, and we can solve the problem using popular algorithms such as SGD, RMSProp, and Adam. The experiment on the image classification with VGG16 shows that the proposed method could identify relevant parts of the images effectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
