| 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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| 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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