论文标题
胶囊之间不需要路由
No Routing Needed Between Capsules
论文作者
论文摘要
大多数胶囊网络设计依赖于胶囊层和计算昂贵的路由机制之间的传统矩阵乘法来处理矩阵乘法引入的胶囊尺寸纠缠。通过使用使用元素乘法而不是矩阵乘法的均匀矢量胶囊（HVC），胶囊的尺寸保持未插入。在这项工作中，我们研究了应用于高度结构化MNIST数据集的HVC，以直接比较Geoffrey Hinton等人的胶囊研究方向。在我们的研究中，我们表明，使用HVCS的简单卷积神经网络以及先前的最佳性能胶囊网络在MNIST上使用5.5倍的参数，较少的参数少4倍，没有重建子网络，并且不需要路由机制。在网络中添加多个分类分支为MNIST数据集建立了新的最新技术状态，这些模型的合奏精度为99.87％，并为单个模型（准确99.83％）建立了新的最新技术。
Most capsule network designs rely on traditional matrix multiplication between capsule layers and computationally expensive routing mechanisms to deal with the capsule dimensional entanglement that the matrix multiplication introduces. By using Homogeneous Vector Capsules (HVCs), which use element-wise multiplication rather than matrix multiplication, the dimensions of the capsules remain unentangled. In this work, we study HVCs as applied to the highly structured MNIST dataset in order to produce a direct comparison to the capsule research direction of Geoffrey Hinton, et al. In our study, we show that a simple convolutional neural network using HVCs performs as well as the prior best performing capsule network on MNIST using 5.5x fewer parameters, 4x fewer training epochs, no reconstruction sub-network, and requiring no routing mechanism. The addition of multiple classification branches to the network establishes a new state of the art for the MNIST dataset with an accuracy of 99.87% for an ensemble of these models, as well as establishing a new state of the art for a single model (99.83% accurate).
