Advanced Transfer Learning
Transfer Learning is the practice of using a pre-trained model as a starting point for a new but related task, leveraging learned features to accelerate training and improve performance.
Why Transfer Learning Works:
• Feature Hierarchy - Lower layers learn general features
• Data Efficiency - Requires fewer training examples
• Computational Savings - Faster training and convergence
• Better Performance - Often superior to training from scratch
💡 Core Principle
Features learned on one task often transfer to related tasks. CNNs trained on ImageNet learn edge, texture, and shape detectors useful for many vision tasks.
Transfer Learning Strategies
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🔒
Feature Extraction
Freeze pre-trained layers, train only new classifier
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🎯
Fine-Tuning
Unfreeze some layers and train with very low learning rate
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📈
Progressive Unfreezing
Gradually unfreeze layers from top to bottom
Strategy Selection:
Choice depends on dataset size, similarity to source domain, and computational resources available.