Advantages of Capsule Networks
Compared to more conventional neural network architectures like convolutional neural networks (CNNs), capsule neural networks (CapsNets) have a number of advantages. The following are some of Capsule Networks’ main benefits:
- Improved Handling of Hierarchies: By capturing the spatial hierarchies among features, Capsule Neural Networks (CapsNets) improve representation learning by handling hierarchical relationships in data quickly.
- Reduced Need for Data Augmentation: Compared to traditional CNNs, capsule neural networks, or CapsNets, frequently require less data augmentation. By facilitating consensus on instantiation parameters, dynamic routing lessens the requirement for significant data augmentation in capsules.
- Improved Robustness to Adversial Attacks: CapsNets demonstrate heightened resilience against adversarial attacks due to their use of vector representations and routing methods, making it challenging for attackers to influence the network with minor input perturbations.
- Explicit pose Information: Capsules in CapsNets encode pose information, enhancing structured representations for tasks like object recognition and pose estimation, where understanding spatial relationships is crucial.
Introduction to Capsule Neural Networks | ML
Capsule Neural Network also known as CapsNet is an artificial neural network (ANN) in machine learning to designed to overcome limitations of traditional convolutional neural networks (CNNs). The article explores the fundamentals, working and architecture of CapsNet.
Table of Content
- Limitation of CNN
- What is Capsule Neural Networks?
- Working of a Capsule Network
- What is Dynamic Routing?
- Architecture of Capsule networks