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2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
ISSN: 1063-6919
ISBN: 978-1-4673-6963-3
pp: 4840-4848
Benjamin Klein , The Blavatnik School of Computer Science, Tel Aviv University, Israel
Lior Wolf , The Blavatnik School of Computer Science, Tel Aviv University, Israel
Yehuda Afek , The Blavatnik School of Computer Science, Tel Aviv University, Israel
ABSTRACT
We present a new deep network layer called “Dynamic Convolutional Layer” which is a generalization of the convolutional layer. The conventional convolutional layer uses filters that are learned during training and are held constant during testing. In contrast, the dynamic convolutional layer uses filters that will vary from input to input during testing. This is achieved by learning a function that maps the input to the filters. We apply the dynamic convolutional layer to the application of short range weather prediction and show performance improvements compared to other baselines.
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CITATION

B. Klein, L. Wolf and Y. Afek, "A Dynamic Convolutional Layer for short rangeweather prediction," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 4840-4848.
doi:10.1109/CVPR.2015.7299117
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