Interpretable explanations of black boxes by meaningful perturbation

Conference item

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.

Authors: Fong, RC; Vedaldi, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2017 IEEE International Conference on Computer Vision (ICCV)
• Pages: 3449-3457
• Publication date: 2017-12-25
• Acceptance date: 2017-07-17
• Event title: ICCV 2017 International Conference on Computer Vision
• Event location: Venice, Italy
• Event website: https://iccv2017.thecvf.com/
• Event start date: 2017-10-22
• Event end date: 2017-10-29
• DOI: 10.1109/ICCV.2017.371
• ISSN: 2380-7504
• EISBN: 978-1-5386-1032-9
• ISBN: 978-1-5386-1033-6
• Language: English