In the pursuit of transferring a source model to a target domain without access to the source training data, Source-Free Domain Adaptation (SFDA) has been extensively explored across various scenarios, including closed-set, open-set, partial-set, and generalized settings. Existing methods, focusing on specific scenarios, not only address only a subset of challenges but also necessitate prior knowledge of the target domain, significantly limiting their practical utility and deployability. In light of these considerations, we introduce a more practical yet challenging problem, termed unified SFDA, which comprehensively incorporates all specific scenarios in a unified manner. To tackle this unified SFDA problem, we propose a novel approach called Latent Causal Factors Discovery (LCFD). In contrast to previous alternatives that emphasize learning the statistical description of reality, we formulate LCFD from a causality perspective. The objective is to uncover the causal relationships between latent variables and model decisions, enhancing the reliability and robustness of the learned model against domain shifts. To integrate extensive world knowledge, we leverage a pre-trained vision-language model such as CLIP. This aids in the formation and discovery of latent causal factors in the absence of supervision in the variation of distribution and semantics, coupled with a newly designed information bottleneck with theoretical guarantees. Extensive experiments demonstrate that LCFD can achieve new state-of-the-art results in distinct SFDA settings, as well as source-free out-of-distribution generalization.Our code and data are available at https://github.com/tntek/source-free-domain-adaptation.

翻译：在无需源训练数据的情况下将源模型迁移到目标域的研究中，无源域适应（SFDA）已在包括闭集、开集、部分集和广义设置等多种场景中得到广泛探索。现有方法聚焦于特定场景，不仅仅解决部分挑战，还需要目标域的预先知识，这严重限制了其实用性和可部署性。基于这些考虑，我们引入了一个更具实用性但更具挑战性的问题，称为统一无源域适应，该问题以统一方式全面涵盖所有特定场景。为解决这一统一SFDA问题，我们提出了一种名为潜在因果因素发现（LCFD）的新方法。与以往强调学习现实统计描述的替代方法不同，我们从因果关系角度公式化LCFD。其目标是揭示潜在变量与模型决策之间的因果联系，增强所学模型对域偏移的可靠性和鲁棒性。为整合广泛的世界知识，我们利用预训练的视觉语言模型（如CLIP）。在分布与语义变化缺乏监督的情况下，这有助于形成和发现潜在因果因素，并结合新设计的具有理论保证的信息瓶颈方法。大量实验表明，LCFD能够在不同SFDA设置以及无源域外分布泛化中取得新的最先进结果。我们的代码和数据可在https://github.com/tntek/source-free-domain-adaptation获取。