Few-Shot Unsupervised Domain Adaptation via Meta Learning

Unsupervised domain adaptation (UDA) has raised a lot of interests in recent years. However, current UDA methods are still not capable enough in dealing with two issues: 1) the scarcity of labeled data in source domain and 2) the need of a general model that can quickly adapt to solve new UDA tasks. To address this situation, we investigate available but rarely-studied setting called few-shot unsupervised domain adaptation (FS-UDA), in which the data of source domain is few-shot per category and the data of target domain remains unlabeled. To realize effective adaptation for FS-UDA tasks in the same source and target domains, we propose a novel meta learning method namely meta-FUDA, which leverages meta learning to perform task-level transfer and domain-level transfer jointly. Extensive experiments demonstrate the promising performance of our method on multiple benchmark data sets.