This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed at 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 six widely-used datasets demonstrate the benefits, abilities, and effectiveness of our proposal.

通过在推理过程中利用目标域的无标签数据，本论文提出了三个主要贡献：1）在测试时，通过将伪标签建模为分布来考虑不确定性，从而推广源域训练的模型到目标域；2）学习具有邻近目标样本信息的变分邻居标签以生成更稳健的伪标签；3）引入元泛化阶段以模拟推广过程，学习融合更具代表性目标信息、生成更精确和稳健的变分邻居标签能力。六个广泛使用的数据集实验证明了我们提出方法的优点、能力和有效性。

学习测试时域泛化的变分邻居标签