This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed on unseen target domains. We follow the strict separation of source training and target testing, but exploit the value of the unlabeled target data itself during inference. We make three contributions. First, we propose probabilistic pseudo-labeling of target samples to generalize the source-trained model to the target domain at test time. We formulate the generalization at test time as a variational inference problem, by modeling pseudo labels as distributions, to consider the uncertainty during generalization and alleviate the misleading signal of inaccurate pseudo labels. Second, we learn variational neighbor labels that incorporate the information of neighboring target samples to generate more robust pseudo labels. Third, to learn the ability to incorporate more representative target information and generate more precise and robust variational neighbor labels, we introduce a meta-generalization stage during training to simulate the generalization procedure. Experiments on seven widely-used datasets demonstrate the benefits, abilities, and effectiveness of our proposal.

翻译：本文致力于领域泛化研究，即在模型仅基于源域训练后，部署于未见目标域的场景。我们遵循源域训练与目标域测试严格分离的原则，但在推理阶段充分利用未标注目标数据本身的价值。我们做出三项贡献。首先，我们提出对目标样本进行概率式伪标记，使源域训练模型在测试时能泛化至目标域。我们将测试时泛化建模为变分推断问题，通过将伪标签建模为概率分布来考虑泛化过程中的不确定性，并缓解不准确伪标签产生的误导性信号。其次，我们学习变分近邻标签，通过融合邻近目标样本的信息来生成更鲁棒的伪标签。第三，为提升模型融合更具代表性目标信息的能力，并生成更精确、更鲁棒的变分近邻标签，我们在训练阶段引入元泛化阶段以模拟泛化过程。在七个广泛使用的数据集上的实验验证了我们所提方法的优势、能力及有效性。