Limitation of CNN
Convolutional Neural Networks (CNNs) represent a category of deep learning models specifically tailored for processing structured grid data. Because of their capacity to automatically extract hierarchical representations from input, CNNs—whose design is inspired by the visual processing found in the human brain—have emerged as a key component of deep learning.
Convolutional layers play a major role in the fundamental design of Convolutional Neural Networks (CNNs), which are used to identify hierarchical patterns in images. However, deep learning pioneer Geoffrey Hinton criticizes the widely used max-pooling procedure, which is used to down sample spatial information. Hinton refers to max-pooling as a “big mistake” because, although effective in expanding the network’s field of vision and retrieving high-level characteristics, it discards important information. The success of the pooling operation is viewed as a “disaster” because it conceals the underlying issue with the network, which is the loss of hierarchical structures and geographical relationships.
To address the shortcomings of conventional CNNs, Hinton and his colleagues proposed an alternative called Capsule Networks, which introduce capsules that maintain spatial hierarchies. This allows for better handling of pose variations, deformations, and complex spatial relationships.
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