Two-Dimensional Perceptrons
Jun Ou, Yujian Li · Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence · 2018
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, a TDP could perform similarly or slightly better than a corresponding perceptron and a corresponding convolutional neural network. Hence, although TDP needs further exploring in many respects, it is a promising and potential model that may open some new directions for deep neural networks, particularly alternatives to CNNs.