A capsule network is a type of neural network architecture designed to better model hierarchical relationships and spatial information in data, particularly in computer vision tasks. Unlike traditional convolutional neural networks (CNNs), which rely on scalar outputs (e.g., activation values) and pooling layers that discard positional details, capsule networks use capsules—groups of neurons that output vectors. These vectors encode both the likelihood of an object’s presence (via the vector’s length) and its properties, such as orientation or scale (via the vector’s direction). This approach aims to address limitations in CNNs, such as struggles with overlapping features or variations in object viewpoints.
The core idea revolves around dynamic routing, a mechanism that allows capsules in one layer to send their outputs to higher-level capsules best suited to represent specific entities. For example, in image recognition, a lower-level capsule might detect edges, while a higher-level capsule could combine these edges into shapes like circles or squares. Dynamic routing ensures that relevant features are weighted more heavily, reducing information loss compared to max-pooling in CNNs. A practical example is the original capsule network (CapsNet) applied to MNIST digit classification. The model uses two convolutional layers followed by a primary capsule layer (which outputs vectors) and a digit capsule layer, where each capsule corresponds to a digit class. The routing algorithm iteratively adjusts how lower-level capsules contribute to higher-level ones, improving pose estimation and part-whole relationships.
Capsule networks offer advantages in scenarios requiring spatial awareness, such as detecting objects with deformations or novel orientations. For instance, a rotated or skewed digit in MNIST can still be recognized accurately because capsules encode orientation directly. However, they are computationally intensive due to the dynamic routing process and have seen limited adoption compared to CNNs. Developers exploring capsule networks might implement custom layers in frameworks like TensorFlow or PyTorch, though libraries such as Keras do not natively support capsules. While promising for tasks like medical imaging (where precise spatial relationships matter), their practical use remains niche, often requiring specialized architectures and tuning. Despite this, they represent a distinct approach to preserving spatial hierarchies in deep learning.