Unsupervised domain adaptation(UDA) and Source-free UDA(SFUDA) methods formulate the problem involving two domains: source and target. They typically employ a standard training approach that begins with models pre-trained on large-scale datasets e.g., ImageNet, while rarely discussing its effect. Recognizing this gap, we investigate the following research questions: (1) What is the correlation among ImageNet, the source, and the target domain? (2) How does pre-training on ImageNet influence the target risk? To answer the first question, we empirically observed an interesting Spontaneous Pulling (SP) Effect in fine-tuning where the discrepancies between any two of the three domains (ImageNet, Source, Target) decrease but at the cost of the impaired semantic structure of the pre-train domain. For the second question, we put forward a theory to explain SP and quantify that the target risk is bound by gradient disparities among the three domains. Our observations reveal a key limitation of existing methods: it hinders the adaptation performance if the semantic cluster structure of the pre-train dataset (i.e.ImageNet) is impaired. To address it, we incorporate ImageNet as the third domain and redefine the UDA/SFUDA as a three-player game. Specifically, inspired by the theory and empirical findings, we present a novel framework termed TriDA which additionally preserves the semantic structure of the pre-train dataset during fine-tuning. Experimental results demonstrate that it achieves state-of-the-art performance across various UDA and SFUDA benchmarks.

以无监督域适应和无源自适应方法为研究对象，通过研究ImageNet与源域及目标域之间的相关性及ImageNet对目标风险的影响，发现现有方法存在的关键限制，并提出了一种新的框架TriDA，通过保留预训练数据集（即ImageNet）的语义结构来提升适应性能。实验证明，TriDA在各种无监督域适应和无源自适应评估中取得了最先进的性能。

非监督领域适应中融合预训练数据的重要性