Disadvantages of Capsule Networks
Although Capsule Neural Networks (CapsNets) have demonstrated encouraging benefits, they are not without obstacles and drawbacks:
- Limited Empirical Evidence: Compared to more well-established designs such as CNNs, CapsNets are a relatively new concept and so have not been thoroughly empirically tested. To confirm their general efficacy across a range of jobs, more investigation and benchmarking are needed.
- Computational Complexity: Because dynamic routing is iterative, CapsNets may require more computing power, especially when dealing with large datasets. This could lead to longer training durations and more resource requirements.
- Potential Overemphasis on Capsules: The concentration of CapsNets on capsules may cause other important aspects of network architecture to be overlooked. For best results, the capsule layers and other components must be properly balanced.
- Inherent Complexity of Capsules: The additional complexity of capsules could not improve performance, especially for smaller workloads or datasets when standard architectures are enough. CapsNets could be excessively complex for specific uses.
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