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Abstract:
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.
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Source :
SOFT COMPUTING
ISSN: 1432-7643
Year: 2020
Issue: 5
Volume: 24
Page: 3355-3364
4 . 1 0 0
JCR@2022
ESI Discipline: COMPUTER SCIENCE;
ESI HC Threshold:132
Cited Count:
WoS CC Cited Count: 3
SCOPUS Cited Count: 3
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 2
Affiliated Colleges: