Unsupervised domain adaptation (UDA) has witnessed remarkable advancements in improving the accuracy of models for unlabeled target domains. However, the calibration of predictive uncertainty in the target domain, a crucial aspect of the safe deployment of UDA models, has received limited attention. The conventional in-domain calibration method, \textit{temperature scaling} (TempScal), encounters challenges due to domain distribution shifts and the absence of labeled target domain data. Recent approaches have employed importance-weighting techniques to estimate the target-optimal temperature based on re-weighted labeled source data. Nonetheless, these methods require source data and suffer from unreliable density estimates under severe domain shifts, rendering them unsuitable for source-free UDA settings. To overcome these limitations, we propose PseudoCal, a source-free calibration method that exclusively relies on unlabeled target data. Unlike previous approaches that treat UDA calibration as a \textit{covariate shift} problem, we consider it as an unsupervised calibration problem specific to the target domain. Motivated by the factorization of the negative log-likelihood (NLL) objective in TempScal, we generate a labeled pseudo-target set that captures the structure of the real target. By doing so, we transform the unsupervised calibration problem into a supervised one, enabling us to effectively address it using widely-used in-domain methods like TempScal. Finally, we thoroughly evaluate the calibration performance of PseudoCal by conducting extensive experiments on 10 UDA methods, considering both traditional UDA settings and recent source-free UDA scenarios. The experimental results consistently demonstrate the superior performance of PseudoCal, exhibiting significantly reduced calibration error compared to existing calibration methods.

翻译：无监督领域自适应（UDA）在提升面向无标注目标域的模型准确性方面取得了显著进展。然而，作为确保UDA模型安全部署的关键因素，目标域预测不确定性的校准问题却鲜受关注。传统的域内校准方法——温度缩放（TempScal）——因领域分布偏移及缺乏带标注目标域数据而面临挑战。近期方法采用重要性加权技术，基于重新加权的带标签源数据估计目标域最优温度。但这类方法不仅需要源数据，且在严重领域偏移下易产生不可靠的密度估计，因此不适用于无源UDA场景。为克服这些限制，本文提出PseudoCal——一种仅依赖无标注目标数据的无源校准方法。与将UDA校准视为协变量偏移问题的传统思路不同，我们将其定位为面向目标域的特定无监督校准问题。受TempScal中负对数似然（NLL）目标函数分解的启发，我们生成能捕获真实目标域结构的带标签伪目标集，从而将无监督校准问题转化为有监督问题，进而可借助TempScal等广泛应用的域内方法有效求解。最后，通过在10种UDA方法（涵盖传统UDA设置与近期无源UDA场景）上开展大量实验，全面评估了PseudoCal的校准性能。实验结果表明，PseudoCal始终表现出卓越性能，其校准误差较现有校准方法显著降低。