  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.
