Reservoir-based convolution

DOI IR HANDLE Web Site 1 Citations 12 References Open Access
  • Tanaka Yuichiro
    Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology
  • Tamukoh Hakaru
    Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology

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<p>Reservoir computing (RC) has attracted attention and has been used in many applications because of its low training cost. Multiple studies using RC for image recognition have been proposed, and some have achieved accuracy rates of greater than 99% on the MNIST dataset. For the Fashion-MNIST and CIFAR-10 datasets, however, they have not yet achieved high accuracy. This study proposes a novel convolutional neural network based on RC that can be optimized by ridge regression rather than back-propagation. The reservoir-based network has multiple reservoirs with various leak rates to extract features with various spatial frequencies from the inputs. The experimental results show that the performance of the proposed model achieves higher accuracy rates in the mentioned datasets compared with those of other reservoir-based image recognition approaches.</p>

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