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TDP: Two-dimensional perceptron for image recognition
TDP: پرسپترون دو بعدی برای تشخیص تصویر-2020 Convolutional neural network (CNN) is widely applied to different areas due to good recognition
performance. However, convolution operation is a complex computation and consumes the bulk of
processing time for CNN. It is still a hot problem how to develop a novel model with good recognition
performance for deep learning. Here, we propose a novel model, namely, two-dimensional perceptron
(TDP), to get direct input of two-dimensional data for further processing. A TDP has a new network
architecture and an innovative computation process of hidden neurons. In cases with the same
number of hidden neurons, compared with multilayer perceptron (MLP), TDP achieves good recognition
performance with 1×-36× speedup and a decrease of parameters by exceeding 97% on MNIST and
COIL-20 datasets. Meanwhile, TDP obtains 1%–32% improvement of recognition accuracy in comparison
to CNN on CIFAR-10 and SVHN datasets. Furthermore, on INFUSE dataset, TDP has an increase of
F1 score by up to almost 11% in comparison with MLP and CNN. The results indicate that TDP is a
promising and novel model with excellent recognition performance. Keywords: Convolutional neural network | Multilayer perceptron | Two-dimensional perceptron | Recognition performance | F1 score |
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