• 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