Presentation 2006-03-16
Sparse Code Learning in Hyper-Column Model
Atsushi SHIMADA, Naoyuki TSURUTA, Rin-ichiro TANIGUCHI,
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Abstract(in English) Hyper-Column Model (HCM) is a self-organized, competitive and hierarchical multilayer neural network. It is derived from the Neocognitron by replacing each S cell and C cell with a two layer Hierarchical Self-Organizing Map (HSOM). HCM can recognize images with variant object size, position, orientation and spatial resolution. Original HCM has connections only with its input layer or bottom layer and does not assume lateral connections among HSOMs. In this paper, we propose a new learning method with excitatory lateral connections. HCM can learn patterns of winner neurons which are activated in each HSOM by updating the excitatory connections, which improves the recognition accuracy. In recognition phase, HCM imposes constraints on the neurons which are activated in each HSOM by using the excitatory connections. We call this learning method "Sparse Code Learning" from the viewpoint that an entire activation pattern is described as multiple neurons.
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Keyword(in English) Image recognition / Artificial neural network / Hyper-Column Model / Sparse coding
Paper # PRMU2005-249
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Conference Information
Committee PRMU
Conference Date 2006/3/9(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Sparse Code Learning in Hyper-Column Model
Sub Title (in English)
Keyword(1) Image recognition
Keyword(2) Artificial neural network
Keyword(3) Hyper-Column Model
Keyword(4) Sparse coding
1st Author's Name Atsushi SHIMADA
1st Author's Affiliation Department of Intelligent Systems, Kyushu University()
2nd Author's Name Naoyuki TSURUTA
2nd Author's Affiliation Department of Electronics Engineering and Computer Science, Fukuoka University
3rd Author's Name Rin-ichiro TANIGUCHI
3rd Author's Affiliation Department of Intelligent Systems, Kyushu University
Date 2006-03-16
Paper # PRMU2005-249
Volume (vol) vol.105
Number (no) 673
Page pp.pp.-
#Pages 6
Date of Issue