  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.
