Class activation heatmap
A class activation heatmap is a spatial visualization showing which image regions contribute most strongly to a model’s prediction of a class.
Deep Learning with Python, Third Edition
A visualization technique that highlights image regions relevant to a specific predicted class by producing class-specific activation heatmaps.
- How it is used
- Explains classification decisions, supports model debugging, and helps localize objects within images.
- How deep it goes
- Defines class-specific spatial scores and works through Grad-CAM construction, normalization, and overlay without surveying alternative methods.
- Examples
- Uses dogs-versus-cats classification and a pretrained Xception model analyzing two African elephants.
- What you can do after
- Diagnose/debug with Grad-CAM by generating, normalizing, and overlaying class-specific evidence on an input image.
Read how Deep Learning with Python, Third Edition explains class activation heatmap →
Deep Learning with R, Third Edition
Class activation map visualization is a technique that produces spatial heatmaps indicating which input locations support a model's predicted class.
- How it is used
- Uses spatial evidence to interpret classification decisions, debug mistakes, and localize objects in images.
- How deep it goes
- Explains Grad-CAM through feature maps, gradients, pooling, channel weighting, and image overlays without deriving the method mathematically.
- Examples
- Demonstrates an Xception prediction and heatmap for two African elephants, including an overlay and attention to the calf’s ears.
- What you can do after
- Diagnoses image-classification decisions with Grad-CAM using R, KerasHub, convolutional outputs, gradients, and visual overlays.
Read how Deep Learning with R, Third Edition explains class activation heatmap →
Related concepts
- poolingproduced by · Pooling is an operation that aggregates values or representations across regions or axes to produce a reduced or summarized representation.
- interpretabilityaddresses · Interpretability is the degree to which people can understand a model’s behavior, outputs, predictions, or the factors that produce them.
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