Reference 4 stops to get here

Capsule Network

An architecture using capsules (groups of neurons) that preserve spatial relationships, addressing limitations of CNNs.

Your route here

4 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  3. Convolution ✓ understood

    A mathematical operation that applies filters/kernels to input data to extract features like edges, textures, and patterns.

  4. Convolutional Neural Network ✓ understood

    A neural network that scans images with small learned filters, reusing the same weights at every position to build up from edges to whole objects.

  5. Capsule Network · you are here ✓ understood

An architecture using capsules (groups of neurons) that preserve spatial relationships, addressing limitations of CNNs.

This concept is essential for understanding neural networks & deep learning and forms a key part of modern AI systems.

  • CNN
  • Dynamic Routing
  • Spatial Hierarchy

Where it sits

Before this

Convolutional Neural Network
Capsule Network

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Nothing yet: a destination in its own right.

Explore nearby

Neural Networks Pooling A down-sampling operation in CNNs that reduces spatial dimensions while retaining important features (max pooling, average pooling). Neural Networks Feature Map The output of applying a convolutional filter to an input, representing detected features at various spatial locations. Vision & Multimodal Inception A CNN architecture (GoogLeNet) using parallel convolutions of different sizes to capture multi-scale features efficiently. Vision & Multimodal Vision Transformer Applying the transformer architecture to computer vision by treating image patches as tokens, achieving state-of-the-art results.