Capsule networks emerged as a promising alternative to convolutional neural networks for
learning object-centric representations. The idea is to explicitly model part-whole hierarchies …

Capsule networks (CapsNets) aim to parse images into a hierarchy of objects, parts, and
their relationships using a two-step process involving part–whole transformation and …

In this paper, we present Combined-CapsNet (C-CapsNet), a novel approach aimed at
enhancing the performance and explainability of Capsule Neural Networks (CapsNets) in …

Individual cattle identification is pivotal for dairy farming, food quality tracing, disease
prevention and control, and registration against fraudulent insurance claims. When …

Abstract Capsule Neural Networks (CapsNets) have been re-introduced as a more compact
and interpretable alternative to standard deep neural networks. While recent efforts have …

This paper presents a comparative analysis of the performance of convolutional and capsule
neural networks on a dataset with decreasing sizes of training data. The study evaluates …

Convolutional neural networks have been widely successful in various computer vision
tasks. However, they lack an explicit representation of entities, and the loss of spatial …

The interest in capsule networks, recently proposed as an alternative to convolutional neural
networks (CNNs), has seen a steady increase in recent years. This is mainly due to their …