  Models trained on one set of domains often suffer performance drops on unseen
domains, e.g., when wildlife monitoring models are deployed in new camera
locations. In this work, we study principles for designing data augmentations
for out-of-domain (OOD) generalization. In particular, we focus on real-world
scenarios in which some domain-dependent features are robust, i.e., some
features that vary across domains are predictive OOD. For example, in the
wildlife monitoring application above, image backgrounds vary across camera
locations but indicate habitat type, which helps predict the species of
photographed animals. Motivated by theoretical analysis on a linear setting, we
propose targeted augmentations, which selectively randomize spurious
domain-dependent features while preserving robust ones. We prove that targeted
augmentations improve OOD performance, allowing models to generalize better
with fewer domains. In contrast, existing approaches such as generic
augmentations, which fail to randomize domain-dependent features, and
domain-invariant augmentations, which randomize all domain-dependent features,
both perform poorly OOD. In experiments on three real-world datasets, we show
that targeted augmentations set new states-of-the-art for OOD performance by
3.2-15.2 percentage points.
