Label scarcity refers to the limited availability of labeled training data in supervised machine learning tasks. This challenge is particularly pronounced in domains where acquiring labeled data is expensive, time-consuming, or requires specialized knowledge, leading to difficulties in training effective models. In the context of domain adaptation, label scarcity emphasizes the need to adapt models trained on a source domain with abundant labeled data to a target domain that lacks sufficient labeled examples.
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