Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task

Bachelor thesis (2020)

Authors

David Cian Electrical Engineering, Mathematics and Computer Science

Contributors

J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS (mentor)
A. Lengyel Pattern Recognition and Bioinformatics - EEMCS (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 David Cian
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Published Date

15-07-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In this paper, we run two methods of explanation, namely LIME and Grad-CAM, on a convolutional neural network trained to label images with the LEGO bricks that are visible in them. We evaluate them on two criteria, the improvement of the network's core performance and the trust they are able to generate for users of the system. We nd that in general, Grad-CAM seems to outperform LIME on this specic task: it yields more detailed insight from the point of view of core performance and 80% of respondents asked to choose between them when it comes to the trust they inspire in the model choose Grad-CAM. However, we also posit that it is more useful to employ these two methods together, as the insights they yield are complementary.

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