Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation. However, these methods suffer from the following limitations: 1) they require unlabeled data from the target domain, which may not be available at the stage of calibration in real-world applications and 2) their performance depends heavily on the disparity between the distributions of the source and target domains. To address these two limitations, we present novel calibration solutions via domain generalization. Our core idea is to leverage multiple calibration domains to reduce the effective distribution disparity between the target and calibration domains for improved calibration transfer without needing any data from the target domain. We provide theoretical justification and empirical experimental results to demonstrate the effectiveness of our proposed algorithms. Compared against state-of-the-art calibration methods designed for domain adaptation, we observe a decrease of 8.86 percentage points in expected calibration error or, equivalently, an increase of 35 percentage points in improvement ratio for multi-class classification on the Office-Home dataset.

翻译：现有校准算法通过不受监督的域适应处理共变式转换问题,但是,这些方法受到以下限制:(1) 需要目标域的未标记数据,在现实应用的校准阶段可能无法获得这些数据;(2) 其性能在很大程度上取决于源和目标域的分布差异。为了解决这两个限制,我们通过域的笼统化提出新的校准解决方案。我们的核心思想是利用多个校准域缩小目标和校准域之间的有效分布差异,以改进校准转让,而不需要目标域的任何数据。我们提供了理论依据和经验实验结果,以证明我们提议的算法的有效性。与为领域适应而设计的最新校准方法相比,我们观察到预期校准误差减少了8.86个百分点,或者相当于在改进办公室-主机数据集多级分类的比率方面增加了35个百分点。