  The ability to generalize out-of-domain (OOD) is an important goal for deep
neural network development, and researchers have proposed many high-performing
OOD generalization methods from various foundations. While many OOD algorithms
perform well in various scenarios, these systems are evaluated as
``black-boxes''. Instead, we propose a flexible framework that evaluates OOD
systems with finer granularity using a probing module that predicts the
originating domain from intermediate representations. We find that
representations always encode some information about the domain. While the
layerwise encoding patterns remain largely stable across different OOD
algorithms, they vary across the datasets. For example, the information about
rotation (on RotatedMNIST) is the most visible on the lower layers, while the
information about style (on VLCS and PACS) is the most visible on the middle
layers. In addition, the high probing results correlate to the domain
generalization performances, leading to further directions in developing OOD
generalization systems.
