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arXiv · 2001.11366

Black-Box Saliency Map Generation Using Bayesian Optimisation

Mamuku Mokuwe·Michael Burke·Anna Sergeevna Bosman
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Abstract

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.

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Mamuku Mokuwe, Michael Burke, Anna Sergeevna Bosman. 2020-01-30. Black-Box Saliency Map Generation Using Bayesian Optimisation. https://arxiv.org/abs/2001.11366

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