CS5720 - Week 4
Slide 79 of 80

CNN vs Fully Connected Networks

Traditional Approach

Fully Connected Networks

  • 📊
    Massive Parameters: Millions of weights for even small images
  • 🗂️
    No Spatial Structure: Treats pixels as independent features
  • 📍
    Position Dependent: Same object in different locations = different patterns
  • ⚠️
    Prone to Overfitting: Too many parameters for typical datasets
  • 💾
    Memory Intensive: Requires enormous RAM for large images
  • 📈
    Poor Scaling: Parameters grow quadratically with image size
Modern Approach

Convolutional Neural Networks

  • ⚡
    Parameter Efficient: Shared weights dramatically reduce parameters
  • 🎯
    Spatial Awareness: Preserves and leverages spatial relationships
  • 🔄
    Translation Invariant: Recognizes patterns regardless of position
  • ✅
    Better Generalization: Learns robust, generalizable features
  • 🚀
    Memory Efficient: Reasonable memory requirements
  • 📏
    Excellent Scaling: Parameters grow linearly with complexity

Detailed Performance Comparison

Metric Fully Connected CNN Winner
Parameter Count (224×224 input) ~150 Million ~25 Million CNN 🏆
Memory Usage ~600 MB ~100 MB CNN 🏆
ImageNet Accuracy ~78% ~95% CNN 🏆
Training Time Very Slow Moderate CNN 🏆
Overfitting Risk Very High Manageable CNN 🏆
Scalability Poor Excellent CNN 🏆
← Previous Next →
Prepared by Dr. Gorkem Kar