# Increasing the adversarial robustness and explainability of capsule networks with $\gamma$-capsules

## TL;DR

This paper introduces $eta$-capsule networks, a new inductive bias inspired by TE neurons, which enhances adversarial robustness and explainability of capsule networks through a novel routing algorithm and theoretical framework.

## Contribution

It proposes $eta$-capsule networks with a formal framework, a new routing algorithm, and demonstrates improved robustness and explainability over standard capsule networks.

## Key findings

- $eta$-capsule networks are more robust against adversarial attacks.
- They offer increased transparency and interpretability.
- Theoretical metrics validate the effectiveness of the new inductive bias.

## Abstract

In this paper we introduce a new inductive bias for capsule networks and call networks that use this prior $\gamma$-capsule networks. Our inductive bias that is inspired by TE neurons of the inferior temporal cortex increases the adversarial robustness and the explainability of capsule networks. A theoretical framework with formal definitions of $\gamma$-capsule networks and metrics for evaluation are also provided. Under our framework we show that common capsule networks do not necessarily make use of this inductive bias. For this reason we introduce a novel routing algorithm and use a different training algorithm to be able to implement $\gamma$-capsule networks. We then show experimentally that $\gamma$-capsule networks are indeed more transparent and more robust against adversarial attacks than regular capsule networks.