Abstract:
Convolutional neural networks (CNNs) have become a key asset to most of fields in AI. Despite their successful per- formance, CNNs suffer from a major drawback. They fail to capture the hierarchy of spatial relation among different parts of an entity. As a remedy to this problem, the idea of cap- sules was proposed by Hinton. In this paper, we propose the SubSpace Capsule Network (SCN) that exploits the idea of capsule networks to model possible variations in the appear- ance or implicitly-defined properties of an entity through a group of capsule subspaces instead of simply grouping neu- rons to create capsules. A capsule is created by projecting an input feature vector from a lower layer onto the capsule sub- space using a learnable transformation. This transformation finds the degree of alignment of the input with the properties modeled by the capsule subspace.
We show that SCN is a general capsule network that can successfully be applied to both discriminative and genera- tive models without incurring computational overhead com- pared to CNN during test time. Effectiveness of SCN is eval- uated through a comprehensive set of experiments on su- pervised image classification, semi-supervised image classi- fication and high-resolution image generation tasks using the generative adversarial network (GAN) framework. SCN sig- nificantly improves the performance of the baseline models in all 3 tasks.
The base of the code has been borrowed from