# SwGridNet: A Deep Convolutional Neural Network based on Grid Topology for Image Classification Source: [SwGridNet: A Deep Convolutional Neural Network based on Grid Topology for Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:48:18.501Z Authors: Atsushi Takeda Published: 2017-09-22T09:24:08.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()