  Deep neural network has been ensured as a key technology in the field of many
challenging and vigorously researched computer vision tasks. Furthermore,
classical ResNet is thought to be a state-of-the-art convolutional neural
network (CNN) and was observed to capture features which can have good
generalization ability. In this work, we propose a biologically inspired deep
residual neural network where the hexagonal convolutions are introduced along
the skip connections. The performance of different ResNet variants using square
and hexagonal convolution are evaluated with the competitive training strategy
mentioned by [1]. We show that the proposed approach advances the baseline
image classification accuracy of vanilla ResNet architectures on CIFAR-10 and
the same was observed over multiple subsets of the ImageNet 2012 dataset. We
observed an average improvement by 1.35% and 0.48% on baseline top-1 accuracies
for ImageNet 2012 and CIFAR-10, respectively. The proposed biologically
inspired deep residual networks were observed to have improved generalized
performance and this could be a potential research direction to improve the
discriminative ability of state-of-the-art image classification networks.
