Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Black-Box Saliency Map Generation Using Bayesian Optimisation   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.

3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
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The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph