DocumentCode
303397
Title
Global and local weight sharing
Author
Zhang, David ; Hu, Yu Hen
Author_Institution
Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI, USA
Volume
3
fYear
1996
fDate
3-6 Jun 1996
Firstpage
1494
Abstract
We provide a unified view and comparison of different methods in the invariance problem. We show that the local symmetry network can reduce the number of weights, that the hardware implementation can be localized and that the number of multiplications is far less than in the global symmetry network. The geometric interpretations of global and local networks are also given. Finally, we examine the performance differences of the two network configurations and discuss the improvement on generalization for the local symmetry method
Keywords
feedforward neural nets; generalisation (artificial intelligence); generalization; geometric interpretations; global weight sharing; invariance problem; local symmetry method; local weight sharing; Cost function; Hardware; Joining processes; Neurons; Smoothing methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1996., IEEE International Conference on
Conference_Location
Washington, DC
Print_ISBN
0-7803-3210-5
Type
conf
DOI
10.1109/ICNN.1996.549121
Filename
549121
Link To Document