What Are Filters/Kernels?
Filters (or Kernels) are small matrices of learnable weights that act as feature detectors in CNNs. They slide across the input to detect specific patterns like edges, textures, or more complex features.
Key Characteristics:
• Small Size: Typically 3×3, 5×5, or 7×7
• Shared Weights: Same filter used across entire image
• Feature Specific: Each filter detects one pattern
• Learnable: Weights adjusted during training
• Hierarchical: Simple → Complex features
🔲 Low-Level Features
Edges, corners, simple textures - the building blocks of visual perception
🔷 Mid-Level Features
Shapes, parts, texture patterns - combinations of simple features
🎯 High-Level Features
Objects, faces, complex patterns - semantic understanding
Common Filter Types
🧠 Learning vs Hand-Crafted
While we can design filters manually, CNNs learn optimal filters from data. This is the power of deep learning - discovering features we might never have thought of!