  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.

Machine Learning;Artificial Intelligence