Domainadaptaatioissa
Domainadaptaatioissa, often translated as domain adaptation, is a subfield of machine learning that deals with the problem of transferring knowledge learned from one domain (the source domain) to a different but related domain (the target domain). The core challenge lies in the fact that the data distributions between the source and target domains may differ, leading to a performance degradation when a model trained solely on the source domain is applied to the target domain.
The goal of domain adaptation is to mitigate this distribution shift. This is typically achieved by learning
Domain adaptation is crucial in many real-world applications where labeled data is scarce in the target domain,