Overview
Transfer learning allows models to leverage knowledge gained from one domain to improve performance in another. This is highly efficient as it reduces the amount of data and compute needed for the new task.
Common Use Cases
- Using a pre-trained image recognition model (like ImageNet) for a specific medical imaging task.
- Using a pre-trained language model (like BERT) for sentiment analysis.
Benefits
- Faster training times.
- Better performance with limited data.
- Lower computational costs.