New Learning Rules for Hierarchical Neural Networks for Visual Pattern Recognition
| Project/Area Number |
25330300
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Soft computing
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| Research Institution | Fuzzy Logic Systems Institute |
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Principal Investigator |
FUKUSHIMA KUNIHIKO 一般財団法人ファジィシステム研究所, 研究部, 特別研究員 (90218909)
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| Project Period (FY) |
2013-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2014: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 視覚パターン認識 / 多層神経回路 / deep CNN / ネオコグニトロン / 学習手法 / 内挿ベクトル法 / Add-if-Silent / margined WTA / 視覚情報処理 / Add-if-Silent則 / add-if-silent則 / 自己組織化 |
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| Outline of Final Research Achievements |
We developed new learning rules for the neocognitron, which is a deep convolutional neural network for visual pattern recognition.
For training intermediate layers, the learning rule named AiS (Add-if-Silent) is used. Under the AiS, a new cell is generated and added to the network if all postsynaptic cells are silent in spite of non-silent presynaptic cells. Once a cell is generated, its input connections do not change any more. Thus the training process is very simple and does not require time-consuming repetitive calculation.
For training the deepest layer, we proposed a supervised learning rule called mWTA (margined Winner-Take-All). Every time when a training pattern is presented during the learning, if the result of classification by the WTA is an error, a new cell is generated. Here we put a certain amount of margin to the WTA. The mWTA produces a compact set of cells, with which a high recognition rate can be obtained with a small computational cost.
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Report
(6 results)
Research Products
(37 results)
