Sight-Seeing in the Eyes of Deep Neural Networks

Seyran Khademi; Xiangwei Shi; Tino Mager; Ronald Siebes; Carola Hein; Victor De Boer; Jan Van Gemert

doi:10.1109/eScience.2018.00125

Sight-Seeing in the Eyes of Deep Neural Networks

Seyran Khademi, Xiangwei Shi, Tino Mager, Ronald Siebes, Carola Hein, Victor De Boer, Jan Van Gemert

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	IEEE 14th International Conference on eScience (e-Science)
Subtitle of host publication	[Proceedings]
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	407-408
Number of pages	2
ISBN (Electronic)	9781538691564
DOIs	https://doi.org/10.1109/eScience.2018.00125
Publication status	Published - 2018
Event	14th IEEE International Conference on eScience, e-Science 2018 - Amsterdam, Netherlands Duration: 29 Oct 2018 → 1 Nov 2018

Conference

Conference	14th IEEE International Conference on eScience, e-Science 2018
Country/Territory	Netherlands
City	Amsterdam
Period	29/10/18 → 1/11/18

Keywords

Classification
Convolutional neural network (CNN)
Interpretability
Place recognition
Visualization

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10.1109/eScience.2018.00125

Sight-Seeing in the Eyes of Deep Neural NetworksFinal published version, 640 KBLicence: Article 25fa Dutch Copyright Act

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@inproceedings{798d057ec1ff4064b43876dde2533c5f,

title = "Sight-Seeing in the Eyes of Deep Neural Networks",

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.",

keywords = "Classification, Convolutional neural network (CNN), Interpretability, Place recognition, Visualization",

author = "Seyran Khademi and Xiangwei Shi and Tino Mager and Ronald Siebes and Carola Hein and {De Boer}, Victor and {Van Gemert}, Jan",

year = "2018",

doi = "10.1109/eScience.2018.00125",

language = "English",

pages = "407--408",

booktitle = "IEEE 14th International Conference on eScience (e-Science)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "14th IEEE International Conference on eScience, e-Science 2018 ; Conference date: 29-10-2018 Through 01-11-2018",

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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 IEEE 14th International Conference on eScience (e-Science): [Proceedings]., 8588744, Institute of Electrical and Electronics Engineers Inc., pp. 407-408, 14th IEEE International Conference on eScience, e-Science 2018, Amsterdam, Netherlands, 29/10/18. https://doi.org/10.1109/eScience.2018.00125

Sight-Seeing in the Eyes of Deep Neural Networks. / Khademi, Seyran; Shi, Xiangwei; Mager, Tino et al.
IEEE 14th International Conference on eScience (e-Science): [Proceedings]. Institute of Electrical and Electronics Engineers Inc., 2018. p. 407-408 8588744.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Siebes, Ronald

AU - Hein, Carola

AU - De Boer, Victor

AU - Van Gemert, Jan

PY - 2018

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KW - Classification

KW - Convolutional neural network (CNN)

KW - Interpretability

KW - Place recognition

KW - Visualization

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BT - IEEE 14th International Conference on eScience (e-Science)

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T2 - 14th IEEE International Conference on eScience, e-Science 2018

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ER -

Sight-Seeing in the Eyes of Deep Neural Networks

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