What do you do when your dataset matters, but it is far too small to train a powerful model from scratch?
The core problem is common across AI applications. In fields like medicine, teams may gather valuable domain-specific images, but not millions of them. Training a deep model from zero becomes expensive, slow, and often unrealistic when data labeling is limited.
The key insight is that small specialized datasets do not have to start from zero. If a much larger dataset exists that is similar enough in structure, a model can first learn broad patterns there. That earlier training creates a strong foundation, even if the final task is very different in context.
In practice, domain adaptation means starting with a pre-trained model and then retraining it on your target dataset. This reduces cost, lowers data requirements, and often improves performance because the model begins with useful prior knowledge rather than random guesses.
Domain adaptation lets smaller datasets benefit from larger ones, turning limited data into practical model performance.