Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks. However, target domain labels are not accessible in many real-world scenarios. This led to the development of Unsupervised Domain Adaptation (UDA) methods, which only employ unlabeled target samples. Furthermore, efficiency and privacy requirements may also prevent the use of source domain data during the adaptation stage. This challenging setting, known as Source-Free Unsupervised Domain Adaptation (SF-UDA), is gaining interest among researchers and practitioners due to its potential for real-world applications. In this paper, we provide the first in-depth analysis of the main design choices in SF-UDA through a large-scale empirical study across 500 models and 74 domain pairs. We pinpoint the normalization approach, pre-training strategy, and backbone architecture as the most critical factors. Based on our quantitative findings, we propose recipes to best tackle SF-UDA scenarios. Moreover, we show that SF-UDA is competitive also beyond standard benchmarks and backbone architectures, performing on par with UDA at a fraction of the data and computational cost. In the interest of reproducibility, we include the full experimental results and code as supplementary material.

翻译：微调与域适应已成为将深度学习模型高效迁移至新目标任务的有效策略。然而，在许多实际场景中，目标域标签无法获取。这催生了仅利用无标签目标样本的无监督域适应方法。此外，效率与隐私需求可能进一步阻止在适应阶段使用源域数据。这种被称为无源无监督域适应的挑战性场景，因其在现实应用中的潜力而日益受到研究者和从业者的关注。本文通过涵盖500个模型和74个域对的大规模实证研究，首次深入分析了无源无监督域适应中的主要设计选择。我们指出归一化方法、预训练策略和骨干架构是最关键的因素。基于量化发现，我们提出了应对无源无监督域适应场景的最佳实践方案。此外，我们证明无源无监督域适应在标准基准和骨干架构之外同样具有竞争力，能以极低的数据和计算成本达到与无监督域适应相当的性能。为支持可重复性，我们将完整的实验结果与代码作为补充材料一并提供。