Abstract
Many techniques have been proposed in recent years that attempt to explain results of image classifiers, notably for the case when the classifier is a deep neural network. This paper presents an implementation of the Contextual Importance and Utility method for explaining image classifications. It is an R package that can be used with the most usual image classification models. The paper shows results for typical benchmark images, as well as for a medical data set of gastro-enterological images. For comparison, results produced by the LIME method are included. Results show that CIU produces similar or better results than LIME with significantly shorter calculation times. However, the main purpose of this paper is to bring the existence of this package to general knowledge and use, rather than comparing with other explanation methods.
The work is partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.
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Appendix 1: Source Code for ImageNet Cat Results
Appendix 1: Source Code for ImageNet Cat Results
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Främling, K., Knapic̆, S., Malhi, A. (2021). ciu.image: An R Package for Explaining Image Classification with Contextual Importance and Utility. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds) Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2021. Lecture Notes in Computer Science(), vol 12688. Springer, Cham. https://doi.org/10.1007/978-3-030-82017-6_4
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