What is Transfer Learning?
Transfer Learning:
Using knowledge learned from one task to improve performance on a related task. Like how learning to play piano helps with learning guitar!
In Deep Learning:
• Take a model trained on large dataset (ImageNet)
• Use it as starting point for new task
• Fine-tune for specific application
• Achieve better results with less data/time
Key Insight:
Low-level features (edges, textures) are universal across vision tasks. We can reuse them!
Why Transfer Learning?
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⚡
Faster Training
Train in hours instead of weeks
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📊
Less Data Required
Work with thousands, not millions of images
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🎯
Better Performance
Higher accuracy than training from scratch
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💰
Lower Cost
Fewer GPUs, less computation needed
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🚀
Democratizes AI
State-of-the-art results for everyone