Transfer learning is a technique in AI where a pre-trained model is adapted for a new but related task.
Instead of training a model from scratch, transfer learning leverages existing knowledge, saving time and computational resources.
How Transfer Learning Works
- Select Pre-trained Model: Choose a model trained on a large dataset (e.g., ImageNet).
- Adapt Model: Freeze some layers and fine-tune others for the new task.
- Train on New Data: Use a smaller dataset to train the model on the target task.
- Evaluate and Deploy: Test the model’s performance and deploy it.
Advantages of Transfer Learning
- Reduces training time and computational cost
- Requires less labeled data for new tasks
- Improves model performance with prior knowledge
- Enables rapid deployment in real-world applications
Disadvantages
- Pre-trained models may carry biases from original data
- May not perform well if new task is very different
- Requires careful layer selection and fine-tuning
Real-World Examples
- Image classification using pre-trained CNNs
- Text classification and sentiment analysis with BERT
- Medical imaging with limited labeled data
- Speech recognition adapting large models to new accents
- Object detection using pre-trained YOLO models
Conclusion
Transfer learning accelerates AI development by reusing knowledge from existing models, making it a powerful technique for fast and efficient solutions.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/