Evaluating CNN interpretability on sketch classification

Abraham Theodorus, Meike Nauta, Christin Seifert

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
283 Downloads (Pure)


While deep neural networks (DNNs) have been shown to outperform humans on many vision tasks, their intransparent decision making process inhibits wide-spread uptake, especially in high-risk scenarios. The BagNet architecture was designed to learn visual features that are easier to explain than the feature representation of other convolutional neural networks (CNNs). Previous experiments with BagNet were focused on natural images providing rich texture and color information. In this paper, we investigate the performance and interpretability of BagNet on a data set of human sketches, i.e., a data set with limited color and no texture information. We also introduce a heatmap interpretability score (HI score) to quantify model interpretability and present a user study to examine BagNet interpretability from user perspective. Our results show that BagNet is by far the most interpretable CNN architecture in our experiment setup based on the HI score.

Original languageEnglish
Title of host publication12th International Conference on Machine Vision, ICMV 2019
EditorsWolfgang Osten, Dmitry Nikolaev, Jianhong Zhou
ISBN (Electronic)9781510636439
Publication statusPublished - 31 Jan 2020
Event12th International Conference on Machine Vision, ICMV 2019 - Mercure Hotel Amsterdam City, Amsterdam, Netherlands
Duration: 16 Nov 201918 Nov 2019
Conference number: 12

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference12th International Conference on Machine Vision, ICMV 2019
Abbreviated titleICMV
Internet address


  • Explainable AI
  • Interpretable CNN
  • Quantifying model interpretabilty
  • Sketch classification


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