Capsule Networks - Theory and Implementations: Investigating capsule network theory and implementations for better representing hierarchical structures and spatial relationships
Keywords:
Capsule Networks, Dynamic Routing, Hierarchical StructuresAbstract
Capsule networks, a novel neural network architecture, have emerged as a promising approach for improving the representation of hierarchical structures and spatial relationships in data. Unlike traditional convolutional neural networks (CNNs), capsule networks use capsules, which are groups of neurons that encode various properties of the input, such as pose, deformation, and internal hierarchical relationships. This paper provides a comprehensive overview of capsule network theory and implementations, discussing their advantages, challenges, and applications. We first explain the key concepts behind capsule networks, including dynamic routing, transformation matrices, and routing by agreement. We then review recent advancements in capsule network research, such as dynamic routing improvements, architectural enhancements, and applications in computer vision and natural language processing. Additionally, we discuss implementation considerations, including software frameworks and hardware accelerators. Finally, we highlight future research directions and potential applications of capsule networks in various fields.
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