Pose Convolutional Routing Towards Lightweight Capsule Networks

Chengxin Lv, Guangqi Jiang, Shoukun Xu, Yi Liu

Abstract: An important branch of artificial intelligence systems and architectures is Capsule Networks (CapsNets) have been known extremely large amount of parameters and computation because of the complex capsule routing algorithm, making it difficult deep architectures in the era of deep learning. To address this challenge, in this paper, we propose a simple yet effective capsule routing algorithm. Specifically, we activate the pose of the entity using its activation probability. On top of that, a convolution on the activated pose matrix to learn the high-level capsules’ pose matrices. Activations of the high-level capsules can be digged from their pose matrices via convolution and activation. Such mechanism generates fewer network parameters and lightweight computation, which make it practitable a deep CapsNets architecture. Experiments on CIFAR-10/100, Small-NORB, MINIST and even large-scale benchmark PASCAL VOC 2007, demonstrate the effectiveness of the proposed method.