TY - GEN
T1 - A Dynamic Convolutional Layer for short rangeweather prediction
AU - Klein, Benjamin
AU - Wolf, Lior
AU - Afek, Yehuda
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/14
Y1 - 2015/10/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84959186411&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2015.7299117
DO - 10.1109/CVPR.2015.7299117
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AN - SCOPUS:84959186411
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 4840
EP - 4848
BT - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
PB - IEEE Computer Society
T2 - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Y2 - 7 June 2015 through 12 June 2015
ER -