SwGridNet: A Deep Convolutional Neural Network based on Grid Topology for Image Classification
This work addresses image classification for computer vision researchers by introducing a novel network architecture that approximates state-of-the-art performance, but it appears incremental as it builds on existing CNN paradigms.
The authors tackled image classification by proposing SwGridNet, a convolutional neural network with a grid topology that enhances generalization through multipath architecture, achieving test error rates of 2.95% on CIFAR-10 and 15.67% on CIFAR-100.
Deep convolutional neural networks (CNNs) achieve remarkable performance on image classification tasks. Recent studies, however, have demonstrated that generalization abilities are more important than the depth of neural networks for improving performance on image classification tasks. Herein, a new neural network called SwGridNet is proposed. A SwGridNet includes many convolutional processing units which connect mutually as a grid network where many processing paths exist between input and output. A SwGridNet has high generalization capability because the multipath architecture has the same effect of ensemble learning. As described in this paper, details of the SwGridNet network architecture are presented. Experimentally obtained results presented in this paper show that SwGridNets respectively achieve test error rates of 2.95% and 15.67% in a CIFAR-10 and CIFAR-100 classification tasks. The results indicate that the SwGridNet performance approximates that of state-of-the-art deep CNNs.