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The output of a convolutional layer representing detected features across spatial dimensions.
DESCRIPTION
In convolutional neural networks, each filter produces a feature map expressing activations over spatial positions. Feature maps capture local patterns such as edges or textures at various scales. Their resolution and depth depend on stride, padding, and number of filters. Pooling or downsampling reduces spatial size while preserving salient features. Visualization of feature maps aids model interpretability and debugging. Regularization and architecture choices influence feature map sparsity and specificity.
ALTERNATIVE DEFINITIONS
Stanford CS231n
The result of applying a filter to an input is a feature map (activation map).
Clarifies that filters produce activation maps depicting feature responses spatially.
Wikipedia
Each filter produces a feature map that indicates the presence of specific features at each spatial position.
Highlights spatial localization of learned features.
RELATED TERMS
Convolutional Layer Activation Function Receptive Field Pooling Kernel Stride Padding Feature Extraction Deep Learning Backpropagation
COMPETING TERMS
RELATIONS
- Broader: mAP
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Convolutional neural network
- Stanford CS231n - CNN tutorial and notes
- PyTorch - CNN tutorial with feature maps
- Goodfellow, Bengio, Courville - Deep learning reference
TOP QUESTIONS
What is a Feature Map in CNNs?
A feature map is the spatial activation output of a convolutional filter, showing where specific patterns are detected in the input.
Category: Overview
How do stride and padding affect Feature Maps?
Stride reduces spatial resolution by skipping positions, while padding controls boundary behavior; together they determine feature map size and alignment.
Category: Example
Why visualize Feature Maps?
Visualization reveals what the network learns, helps debug vanishing activations, and improves interpretability by highlighting responsive regions to specific filters.
Category: Requirements
How do Feature Maps relate to pooling?
Pooling aggregates local neighborhoods in feature maps, reducing resolution and emphasizing robustness to small translations and noise.
Category: Overview
What is a common mistake with Feature Maps?
Assuming deeper layers always have more information; they often capture higher-level abstractions with lower spatial resolution and should be interpreted accordingly.
Category: Mistakes
Are Feature Maps sparse?
They can be, depending on activations and regularization. Sparse maps can improve efficiency and interpretability by highlighting selective responses.
Category: Opinion
How do Feature Maps differ from Feature Vectors?
Feature maps have spatial structure, while feature vectors are flattened representations used for classification or downstream tasks.
Category: Alternatives
Do Feature Maps exist outside CNNs?
Yes, analogous activation maps appear in vision transformers’ attention or other architectures, though their structure and interpretation differ.
Category: Relevancy
How to manage Feature Maps in production?
Use profiling tools, save representative activations for debugging, and ensure privacy when visualizations can expose sensitive data.
Category: Management