TDP: Two-dimensional perceptron for image recognition

Abstract

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.