The square kernel is a standard unit for contemporary Convolutional Neural Networks (CNNs), as it fits well on the tensor computation for the convolution operation. However, the retinal ganglion cells in the biological visual system have approximately concentric receptive fields. Motivated by this observation, we propose using the circular kernel with a concentric and isotropic receptive field as an option for convolution operation. We first substitute the $3 \times 3$ square kernels with the corresponding circular kernels or our proposed integrated kernels in the typical ResNet architecture, and the modified models after training yield similar or even competitive performance. We then show the advantages of large circular kernels over the corresponding square kernels in that the difference and the improvement are more distinct. Hence, we speculate that large circular kernels would help find advanced neural network models by the Neural Architecture Search (NAS). To validate our hypothesis, we expand the operation space in several typical NAS methods with convolutions of large circular kernels. Experimental results show that the searched new neural network models contain large circular kernels and significantly outperform the original searched models. The additional empirical analysis also reveals that the large circular kernel help the model to be more robust to rotated or sheared images due to its rotation invariance.

翻译：方核内核是当代革命神经网络的标准单元,因为它在革命行动的变速计算中非常适合。然而,生物视觉系统中的视网膜交织细胞具有近似共心的容容场。受此观察的驱使,我们提议使用具有同心和异心容的圆心内核作为共变行动的选项。我们首先将3美元方内核替换为相应的圆心内核,或我们在典型的ResNet结构中拟议的综合内核,在培训后经过修改的模型产生类似甚至竞争性的性能。我们然后展示在相应的方内核上大型圆内核的优点,因为两者的差别和改进更加不同。因此,我们推测,大型圆心内核有助于通过神经结构搜索找到先进的神经网络模型。为了证实我们的假设,我们扩大了一些典型的NAS操作空间,在大型圆心内核结构中,在培训后经过修改的模型产生类似甚至竞争性的性能。实验结果显示,搜索的新圆内核内核内核内核内的圆内核内核实验性模型也包含新的大型循环模型。