论文标题
使用贝叶斯优化的黑盒显着图生成
Black-Box Saliency Map Generation Using Bayesian Optimisation
论文作者
论文摘要
显着图通常用于计算机视觉中，以提供对模型用于产生特定预测的输入区域的直观解释。可以使用许多显着图的方法，但是大多数都需要访问模型参数。这项工作提出了一种使用贝叶斯优化采样方法的黑框模型的显着性图生成的方法，该方法无需访问模型参数。该方法旨在找到负责特定（Black-Box）模型预测的全局显着图像区域。这是通过基于抽样的方法来模型扰动来实现的，该方法试图将图像的显着区域定位到黑盒模型。结果表明，提出的显着性图生成方法的方法优于基于网格的扰动方法，并且与需要访问模型参数的基于梯度的方法相似。
Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches to saliency map generation are available, but most require access to model parameters. This work proposes an approach for saliency map generation for black-box models, where no access to model parameters is available, using a Bayesian optimisation sampling method. The approach aims to find the global salient image region responsible for a particular (black-box) model's prediction. This is achieved by a sampling-based approach to model perturbations that seeks to localise salient regions of an image to the black-box model. Results show that the proposed approach to saliency map generation outperforms grid-based perturbation approaches, and performs similarly to gradient-based approaches which require access to model parameters.
