What are capsule networks, and how do they address limitations of traditional convolutional neural networks (CNNs)?

Capsule networks are a type of neural network architecture designed to overcome limitations of traditional convolutional neural networks (CNNs) in tasks such as object recognition and pose estimation. Capsule networks use dynamic routing between capsules (groups of neurons) to preserve spatial hierarchies and relationships between parts and wholes in images. This allows capsule networks to better handle variations in object pose, scale, and orientation compared to CNNs.