Visualizing CNN Features
CNNs are often called "black boxes," but we can actually peer inside and understand what they're learning at each layer. Let's explore the fascinating world of feature visualization!
🔍 Key Insight: Early layers detect simple patterns (edges, colors), while deeper layers combine these into complex objects and concepts.
What we can visualize:
• Filter weights - What patterns each filter detects
• Feature maps - Network activations for specific inputs
• Class activation maps - Which regions matter for predictions
• Maximally activating patches - Real image regions that excite neurons most
Visualization Techniques
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🎯 Grad-CAM
Class Activation Maps using gradients to highlight important regions
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🔬 Filter Visualization
Visualize what patterns individual filters are looking for
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⚡ Activation Maximization
Generate synthetic images that maximally activate specific neurons
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🔄 Feature Inversion
Reconstruct images from feature representations