What are the methods of domain adaptation in machine learning?

What are the methods of domain adaptation in machine learning?

Domain adaptation refers to techniques that help a model trained on one domain perform well on a different but related domain. Common methods include fine-tuning, feature alignment, and adversarial training.

Can you explain fine-tuning in more detail?

Fine-tuning involves taking a pre-trained model and continuing the training process on a new dataset from the target domain. This helps the model adjust its weights to better fit the new data.

What is feature alignment?

Feature alignment aims to reduce the discrepancy between the feature distributions of the source and target domains, often using techniques like domain-invariant feature extraction.

How does adversarial training work in this context?

Adversarial training involves training a model to be robust against adversarial examples, which can help it generalize better across different domains by making it learn domain-invariant features.