Spatial hierarchy
A spatial hierarchy is a layered or nested organization of spatial information in which representations capture progressively different extents, resolutions, or levels of detail.
Deep Learning with R, Third Edition
A spatial hierarchy is an organization in which increasingly complex visual patterns are composed from simpler patterns across neural-network layers.
- How it is used
- Explains how convolutional networks organize image features from local patterns into increasingly abstract visual concepts.
- How deep it goes
- Connects layerwise pattern composition to pooling, receptive-field growth, and feature-map size, without deriving convolution internals.
- Examples
- Uses edges, textures, object parts, cats, and MNIST digit networks with and without max-pooling.
- What you can do after
- Evaluate convolutional architectures by relating pooling and receptive-field growth to spatial hierarchy.
Read how Deep Learning with R, Third Edition explains spatial hierarchy →
Deep Learning with Python, Third Edition
A layered organization of features in which deeper layers represent patterns over increasingly large spatial extents of the input.
- How it is used
- Explains how convolutional networks organize visual representations for image classification.
- How deep it goes
- Explains layer-wise feature composition and downsampling, but defers detailed architectural treatment of pretrained networks.
- Examples
- Uses edges, textures, eyes, ears, cats, MNIST digits, and an ImageNet-trained model reused for furniture classification.
- What you can do after
- Apply it via ConvNet design choices that enlarge spatial coverage while preserving useful visual information.
Read how Deep Learning with Python, Third Edition explains spatial hierarchy →
Related concepts
- hierarchykind of · A hierarchy is an organization of entities into nested or ordered levels connected by parent-child, containment, or broader-to-narrower relationships.
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