capsule networks

**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.