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Two-dimensional perceptrons
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DOI：10.1007/s00500-019-04098-w.png)
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Abstract
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Convolutional neural networks (CNNs) have made remarkable success in image classification. However, it is still an open problem how to develop new models instead of CNNs. Here, we propose a novel model, namely two-dimensional perceptron (TDP), to get direct input of 2D data for further processing. A TDP computes hidden neurons from the input via left/right matrix multiplication, producing left-weighted TDP and right-weighted TDP, respectively. Experimental results on MNIST and COIL-20 datasets show that, in cases with the same number of hidden neurons, the model obtains 5%-45% relative performance improvement and 2 x-36x speedup in comparison with the corresponding multilayer perceptron and convolutional neural network. Hence, it is a promising and potential model that may open some new directions for deep neural networks, particularly alternatives to CNNs.
Keywords:
Neural networks
Multilayer perceptron
Two-dimensional perceptron
Left weight matrix
Right weight matrix
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