Sight-seeing in the eyes of deep neural networks

Conference Paper (2018)
Author(s)

S Khademi (TU Delft - Pattern Recognition and Bioinformatics)

Xiangwei Shi (TU Delft - Pattern Recognition and Bioinformatics)

Tino Mager (TU Delft - History, Form & Aesthetics)

Ronald Maria Siebes (Vrije Universiteit Amsterdam, TU Delft - Pattern Recognition and Bioinformatics)

Carola Hein (TU Delft - History, Form & Aesthetics)

Victor De Boer (Vrije Universiteit Amsterdam)

J.C. Gemert (TU Delft - Pattern Recognition and Bioinformatics)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2018 S. Khademi, X. Shi, Tino Mager, R.M. Siebes, C.M. Hein, Victor De Boer, J.C. van Gemert
DOI related publication
https://doi.org/10.1109/eScience.2018.00125
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 S. Khademi, X. Shi, Tino Mager, R.M. Siebes, C.M. Hein, Victor De Boer, J.C. van Gemert
Research Group
Pattern Recognition and Bioinformatics
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
407-408
ISBN (electronic)
978-153869156-4
Reuse Rights

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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.

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