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.
|Title of host publication||Proceedings - IEEE 14th International Conference on eScience, e-Science 2018|
|Number of pages||2|
|Publication status||Published - 2018|
|Event||14th IEEE International Conference on eScience, e-Science 2018 - Amsterdam, Netherlands|
Duration: 29 Oct 2018 → 1 Nov 2018
|Conference||14th IEEE International Conference on eScience, e-Science 2018|
|Period||29/10/18 → 1/11/18|
Bibliographical noteGreen 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.
- Convolutional neural network (CNN)
- Place recognition