Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings

Understand

Generalizing deep neural networks to new target domains is critical to their real-world utility.

• In practice, it may be feasible to get some target data labeled, but to be cost-effective it is desirable to select a maximally-informative subset via active learning (AL).
• We study the problem of AL under a domain shift, called Active Domain Adaptation (Active DA).
• We demonstrate how existing AL approaches based solely on model uncertainty or diversity sampling are less effective for Active DA.