Capsule Networks (CapsNets) are a neural architecture proposed by Geoffrey Hinton — designed to overcome the limitations of CNNs (specifically max-pooling) by grouping neurons into "capsules" that represent an object's pose and properties, ensuring viewpoint invariance.

What Is a Capsule Network?

• Vector Neurons: Neurons output vectors (length = existence probability, orientation = pose), not scalars.
• Hierarchy: Parts (nose, mouth) vote for a Whole (face).
• Agreement: If predictions agree, the connection is strengthened (Routing-by-Agreement).
• Equivariance: If the object rotates, the capsule vector rotates (preserves info), whereas CNN pooling throws away location info (invariance).

Why It Matters

• Inverse Graphics: Attempts to perform "rendering in reverse" to understand the scene structure.
• Data Efficiency: theoretically requires fewer samples to learn 3D rotations than CNNs.
• Status: While theoretically beautiful, they have not yet beaten Transformers/ConvNets at scale due to training cost.

Capsule Networks are Hinton's vision for robust vision — prioritizing structural understanding over raw texture matching.