An Exploration of the Interaction Between capsules with ResNetCaps models

Abstract

Image recognition is an open challenge in computer vision since its early stages. The application of deep neural networks yielded significant improvements towards its solution. Despite their classification abilities, deep networks need datasets with thousands of labelled images and prohibitive computational capabilities to achieve good performance. To address some of these challenges, the CapsNet neural architecture has been recently proposed as a promising machine learning model for image classification based on the idea of capsules. A capsule is a group of neurons whose output represents the presence of features of the same entity. In this paper, we start from the CapsNet architecture to explore and analyse the interaction between the presence of features within certain, similar classes. This is achieved by means of techniques for the features interaction, working on the outputs of two independent capsule-based models. To understand the importance of the interaction between capsules, extensive experiments have been carried out on four challenging dataset. Results show that the exploitation of capsules interaction yields to performance improvements.