Capsule Networks for Hierarchical Novelty Detection in Object Classification

Abstract

Hierarchical Novelty Detection (HND) refers to assigning labels to objects in a hierarchical category space, where non-leaf labeling represents a novelty detection of that category. By labeling a novel instance in at least one abstract category, more informed decisions can be made by an automated driving (AD) function, resulting in a safer behavior in novel situations. Current approaches are mainly composed of different architectures based on Convolutional Neural Networks (CNNs). Capsule Networks (CNs) were introduced as an alternative to CNNs that expand their capacity in tasks that were previously challenging. We explore the hierarchical nature of CNs and propose a novel approach for hierarchical novelty detection using a unified CN architecture. As a proof-of-concept, we evaluate it on a novelty detection task based on the Fashion-MNIST dataset. We define a misclassification matrix for evaluation of the performance based on a semantically sensible scenario for this dataset. The results show that our method outperforms the main CNN-based methods in the current literature in this task while also giving more flexibility for task-specific tuning and has the potential to reach state-of-the-art status in more complex HND use cases within the AD domain.