Timezone: »
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.
Author Information
Taeyoung Hahn (SNUVL)
Myeongjang Pyeon (Seoul National University)
Gunhee Kim (Seoul National University)
More from the Same Authors
-
2021 : Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes »
Hyunwoo Kim · Byeongchang Kim · Gunhee Kim -
2021 Poster: Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods »
Seohong Park · Jaekyeom Kim · Gunhee Kim -
2015 Poster: Expressing an Image Stream with a Sequence of Natural Sentences »
Cesc C Park · Gunhee Kim