Domain Adaptation
Closing the gap when a model trained on one data distribution gets deployed on a shifted one, without collecting new labels for the new distribution.
Covariate shift means the input distribution changes between the source and target domains while the true input-output relationship stays fixed — unlike concept shift, where the task's actual definition changes. Domain-adversarial training pushes internal features toward a form a domain classifier can't distinguish, so behavior learned on the source domain transfers to the target one.
It's typically framed as unsupervised: the target domain has no labels at all to fine-tune on.