Interpretable Explanations of Black Boxes by Meaningful Perturbation

Ruth C. Fong, Andrea Vedaldi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

279 Scopus citations

Abstract

As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions. In recent years, a number of image saliency methods have been developed to summarize where highly complex neural networks 'look' in an image for evidence for their predictions. However, these techniques are limited by their heuristic nature and architectural constraints. In this paper, we make two main contributions: First, we propose a general framework for learning different kinds of explanations for any black box algorithm. Second, we specialise the framework to find the part of an image most responsible for a classifier decision. Unlike previous works, our method is model-agnostic and testable because it is grounded in explicit and interpretable image perturbations.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3449-3457
Number of pages9
ISBN (Electronic)9781538610329
DOIs
StatePublished - Dec 22 2017
Externally publishedYes
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: Oct 22 2017Oct 29 2017

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2017-October
ISSN (Print)1550-5499

Other

Other16th IEEE International Conference on Computer Vision, ICCV 2017
Country/TerritoryItaly
CityVenice
Period10/22/1710/29/17

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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