论文标题
用于计算机视觉应用的生物学启发的深层残留网络
Biologically inspired deep residual networks for computer vision applications
论文作者
论文摘要
在许多具有挑战性且大力研究的计算机视觉任务的领域，已确保深层神经网络是一项关键技术。此外，经典的重新系统被认为是最先进的卷积神经网络（CNN），并被观察到可以捕获具有良好概括能力的特征。在这项工作中，我们提出了一个以生物学启发的深度残留神经网络，沿着跳过连接引入了六边形卷积。使用[1]提到的竞争训练策略评估了使用方形和六角卷积的不同重新NET变体的性能。我们表明，所提出的方法推进了CIFAR-10上香草重新结构体系结构的基线图像分类精度，并且在Imagenet 2012数据集的多个子集上观察到了这一点。我们观察到ImageNet 2012和CIFAR-10的基线TOP-1精确度的平均提高1.35％和0.48％。观察到拟议的以生物学启发的深度残留网络具有改善的广义性能，这可能是提高最新图像分类网络歧视能力的潜在研究方向。
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.
