A Dynamic Convolutional Layer for short rangeweather prediction

Benjamin Klein, Lior Wolf, Yehuda Afek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
PublisherIEEE Computer Society
Pages4840-4848
Number of pages9
ISBN (Electronic)9781467369640
DOIs
StatePublished - 14 Oct 2015
EventIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States
Duration: 7 Jun 201512 Jun 2015

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume07-12-June-2015
ISSN (Print)1063-6919

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Country/TerritoryUnited States
CityBoston
Period7/06/1512/06/15

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