Machine Learning
domain-adaptation
A technique in machine learning that aims to improve model performance on a target domain by leveraging labeled data from a related source domain.
Expanded definition
Domain Adaptation is a specialized method in transfer learning where a model trained on one domain (source) is adapted to perform well on a different but related domain (target). This is particularly useful when there is a lack of labeled data in the target domain. Techniques may involve adjusting the model to account for differences in the feature distributions between the two domains, thus enhancing its applicability and effectiveness.
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