What is CapsNet?
CapsNet, short for Capsule Network, is a type of neural network architecture designed to address some of the limitations of traditional convolutional neural networks (CNNs). CapsNet introduces the concept of “capsules,” which are specialized groups of neurons that work together to capture complex patterns in the input data. CapsNets are designed to preserve the hierarchical relationships between features in an image and provide better robustness to translation, rotation, and scale variations.
How does CapsNet work?
CapsNet consists of three main components:
- Convolutional layer: This layer is used for feature extraction, similar to a traditional CNN.
- Capsule layer: This layer consists of multiple capsules, each of which represents specific features of the input data. Capsules learn to encode the presence of features and their instantiation parameters (such as location, scale, and rotation) in a high-dimensional vector.
- Dynamic routing: This mechanism allows the network to learn how to route the output of one capsule to the appropriate parent capsule in the next layer, preserving the hierarchical relationships between features.
Additional resources on CapsNet
To learn more about Capsule Networks, you can explore the following resources:
- Dynamic Routing Between Capsules, the original paper by Sara Sabour, Geoffrey Hinton, and Nicholas Frosst
- Capsule Networks: An Improvement to Convolutional Networks, an article providing an overview of CapsNet and its advantages over CNNs
- Understanding Hinton’s Capsule Networks - Part I: Intuition, a comprehensive blog post explaining the intuition behind CapsNet
- Saturn Cloud, for free cloud compute and resources on machine learning algorithms, including CapsNet.