2019/04/15 by Zhao Zhen, Zhen Zhao, Ashley Kleinhans +8
Computer Science · #Advanced Graph Neural Networks #Caching and Content Delivery #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Stochastic Gradient Optimization Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1904.07304
arxiv created 2019/04/15 · openalex publication_date 2019/04/15 · arxiv updated 2019/04/17 · openalex created_date 2019/04/25 · openalex updated_date 2026/07/28
We present a method for fast inference in Capsule Networks (CapsNets) by taking advantage of a key insight regarding the routing coefficients that link capsules between adjacent network layers. Since the routing coefficients are responsible for assigning object parts to wholes, and an object whole generally contains similar intra-class and dissimilar inter-class parts, the routing coefficients tend to form a unique signature for each object class. For fast inference, a network is first trained in the usual manner using examples from the training dataset. Afterward, the routing coefficients associated with the training examples are accumulated offline and used to create a set of "master" routing coefficients. During inference, these master routing coefficients are used in place of the dynamically calculated routing coefficients. Our method effectively replaces the for-loop iterations in the dynamic routing procedure with a single matrix multiply operation, providing a significant boost in inference speed. Compared with the dynamic routing procedure, fast inference decreases the test accuracy for the MNIST, Background MNIST, Fashion MNIST, and Rotated MNIST datasets by less than 0.5% and by approximately 5% for CIFAR10.