In this work, we take a deeper look into the diverse factors that influence the efficacy of modern unsupervised domain adaptation (UDA) methods using a large-scale, controlled empirical study. To facilitate our analysis, we first develop UDA-Bench, a novel PyTorch framework that standardizes training and evaluation for domain adaptation enabling fair comparisons across several UDA methods. Using UDA-Bench, our comprehensive empirical study into the impact of backbone architectures, unlabeled data quantity, and pre-training datasets reveals that: (i) the benefits of adaptation methods diminish with advanced backbones, (ii) current methods underutilize unlabeled data, and (iii) pre-training data significantly affects downstream adaptation in both supervised and self-supervised settings. In the context of unsupervised adaptation, these observations uncover several novel and surprising properties, while scientifically validating several others that were often considered empirical heuristics or practitioner intuitions in the absence of a standardized training and evaluation framework. The UDA-Bench framework and trained models are publicly available at https://github.com/ViLab-UCSD/UDABench_ECCV2024.

本研究针对现代无监督领域适应（UDA）方法的有效性影响因素进行了深入分析，填补了该领域对标准化评估框架的需求。通过开发UDA-Bench框架，我们发现高级骨干网络的适应效益递减，当前方法未充分利用未标记数据，且预训练数据在监督和自监督设置中显著影响下游适应。此工作为无监督适应领域提供了新颖的见解，并验证了众多以往常被视为经验启发或从业者直觉的理论。

UDA-Bench：使用标准化框架重新审视无监督领域适应中的常见假设