Sight-seeing in the eyes of deep neural networks

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Abstract

We address the interpretability of convolutional neural networks (CNNs) for predicting a geo-location from an image. In a pilot experiment we classify images of Pittsburgh vs Tokyo and visualize the learned CNN filters. We found that varying the CNN architecture leads to variating in the visualized filters. This calls for further investigation of the effective parameters on the interpretability of CNNs.

Original languageEnglish
Title of host publicationProceedings - IEEE 14th International Conference on eScience, e-Science 2018
EditorsW. Hazeleger
PublisherIEEE
Pages407-408
Number of pages2
ISBN (Electronic)978-153869156-4
DOIs
Publication statusPublished - 2018
Event14th IEEE International Conference on eScience, e-Science 2018 - Amsterdam, Netherlands
Duration: 29 Oct 20181 Nov 2018

Conference

Conference14th IEEE International Conference on eScience, e-Science 2018
CountryNetherlands
CityAmsterdam
Period29/10/181/11/18

Keywords

  • Classification
  • Convolutional neural network (CNN)
  • Interpretability
  • Place recognition
  • Visualization

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  • Cite this

    Khademi, S., Shi, X., Mager, T., Siebes, R., Hein, C., De Boer, V., & Van Gemert, J. (2018). Sight-seeing in the eyes of deep neural networks. In W. Hazeleger (Ed.), Proceedings - IEEE 14th International Conference on eScience, e-Science 2018 (pp. 407-408). [8588744] IEEE. https://doi.org/10.1109/eScience.2018.00125