A Parameter Efficient Multi-Scale Capsule Network

Capsule networks consider spatial relationships in an input image. The relationship-based feature propagation in capsule networks shows promising results. However, a large number of trainable parameters limit their widespread use. In this paper, we propose Decomposed Capsule Network (DCN) to reduce the number of training parameters in the primary capsule generation stage. Our DCN represents a capsule as a combination of basis vectors. Generating basis vectors and their coefficients notably reduce the total number of training parameters. Moreover, we introduce an extension of the DCN architecture, named Multi-scale Decomposed Capsule Network (MDCN). The MDCN architecture integrates features from multiple scales to synthesize capsules with fewer parameters. Our proposed networks show better performance on the Fashion-MNIST dataset and the CIFAR10 dataset with fewer parameters than the original network.