Black-Box Saliency Map Generation Using Bayesian Optimisation

Autor:	Mokuwe, Mamuku, Burke, Michael, Bosman, Anna Sergeevna
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Submitted to IJCNN 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.11366 Zobrazit plný text záznamu View this record from Arxiv