What are domain adaptation techniques in machine learning?

What are domain adaptation techniques in machine learning?

Domain adaptation techniques are methods used to adapt a model trained on one domain to work effectively on a different but related domain.

Can you give me some examples of these techniques?

Sure! Some common techniques include fine-tuning, feature alignment, and adversarial training.

How does fine-tuning work in domain adaptation?

Fine-tuning involves taking a pre-trained model and continuing the training process on a new dataset from the target domain.

What challenges are associated with domain adaptation?

Challenges include domain shift, where the data distributions differ significantly, and overfitting to the target domain.