Self-Routing Capsule Networks

Capsule networks have recently gained a great deal of interest as a new architecture of neural networks that can be more robust to input perturbations than similar-sized CNNs. Capsule networks have two major distinctions from the conventional CNNs: (i) each layer consists of a set of capsules that specialize in disjoint regions of the feature space and (ii) the routing-by-agreement coordinates connections between adjacent capsule layers. Although the routing-by-agreement is capable of filtering out noisy predictions of capsules by dynamically adjusting their influences, its unsupervised clustering nature causes two weaknesses: (i) high computational complexity and (ii) cluster assumption that may not hold in presence of heavy input noise. In this work, we propose a novel and surprisingly simple routing strategy called self-routing where each capsule is routed independently by its subordinate routing network. Therefore, the agreement between capsules is not required anymore but both poses and activations of upper-level capsules are obtained in a way similar to Mixture-of-Experts. Our experiments on CIFAR-10, SVHN and SmallNORB show that the self-routing performs more robustly against white-box adversarial attacks and affine transformations, requiring less computation.