A capsule network (CapsNet) is a recently proposed neural network model with a new
structure. The purpose of CapsNet is to form activation capsules. In this paper, our team …

Past few years have witnessed exponential growth of interest in deep learning
methodologies with rapidly improving accuracies and reduced computational complexity. In …

In this paper, we propose a new capsule network architecture called Attention Routing
CapsuleNet (AR CapsNet). We replace the dynamic routing and squash activation function …

Capsule Network (CapsNet) addresses the problem of Convolutional Neural Network (CNN)
by introducing dynamic routing between capsules. Our work further develops CapsNet in …

Abstract The Capsule Network is widely believed to be more robust than Convolutional
Networks. However, there lack comprehensive comparisons between these two networks …

The capsule network is a novel architecture to encode feature attributes and spatial
relationships of an image. By using the dynamic routing (DR) algorithm, a capsule network …

Capsule networks (CapsNets) have been known difficult to develop a deeper architecture,
which is desirable for high performance in the deep learning era, due to the complex …

Capsule Network is a promising concept in deep learning, yet its true potential is not fully
realized thus far, providing sub-par performance on several key benchmark datasets with …

Capsule Network is a novel and promising neural network in the field of deep learning,
which has shown good performance in image classification by encoding features into …

In recent years, convolutional neural networks (CNN) have played an important role in the
field of deep learning. Variants of CNN's have proven to be very successful in classification …