  Recently, source-free unsupervised domain adaptation (SFUDA) has emerged as a
more practical and feasible approach compared to unsupervised domain adaptation
(UDA) which assumes that labeled source data are always accessible. However,
significant limitations associated with SFUDA approaches are often overlooked,
which limits their practicality in real-world applications. These limitations
include a lack of principled ways to determine optimal hyperparameters and
performance degradation when the unlabeled target data fail to meet certain
requirements such as a closed-set and identical label distribution to the
source data. All these limitations stem from the fact that SFUDA entirely
relies on unlabeled target data. We empirically demonstrate the limitations of
existing SFUDA methods in real-world scenarios including out-of-distribution
and label distribution shifts in target data, and verify that none of these
methods can be safely applied to real-world settings. Based on our experimental
results, we claim that fine-tuning a source pretrained model with a few labeled
data (e.g., 1- or 3-shot) is a practical and reliable solution to circumvent
the limitations of SFUDA. Contrary to common belief, we find that carefully
fine-tuned models do not suffer from overfitting even when trained with only a
few labeled data, and also show little change in performance due to sampling
bias. Our experimental results on various domain adaptation benchmarks
demonstrate that the few-shot fine-tuning approach performs comparatively under
the standard SFUDA settings, and outperforms comparison methods under realistic
scenarios. Our code is available at https://github.com/daintlab/fewshot-SFDA .
