Definition
Transfer learning pretrains on a large source task (ImageNet, web text), then adapts to a target task via fine-tuning or feature extraction.
It is why modern AI works with limited labeled data — including low-resource languages.
In simple terms
Learning piano makes learning another instrument faster. Transfer learning is that skill carryover for neural networks.
Where you see it
- BERT/GPT pretrained on web text, fine-tuned for classification.
- Vision models pretrained on ImageNet for medical imaging.
- Multilingual models adapted for Somali NLP.
How it works
1.Pretrain
Learn general representations on large data.
2.Adapt
Fine-tune or freeze backbone and train a new head.
3.Evaluate
Confirm gains on the target domain.
Why it matters
- Transfer learning is the default strategy for practical deep learning today.
Often confused
Transfer learning and fine-tuning are unrelated.
Fine-tuning is the most common way to perform transfer learning.